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  • 机器学习相关论文

    2018-02-10 10:44:06
    2017年的年度关于machine learning的文章,通过这些文章可以更好的扩展在相关方面的知识。而且有利于提高个人的技能,并将之运用到实际的问题当中。
  • 点击上方“视学算法”,选择加"星标"或“置顶”重磅干货,第一时间送达转自:新智元好像很多人都觉得读论文做笔记是一件非常正确、重要、且必要的事情。你可能没少看有关《为什么习...

    点击上方视学算法”,选择加"星标"或“置顶”

    重磅干货,第一时间送达

    转自:新智元

    好像很多人都觉得读论文做笔记是一件非常正确、重要、且必要的事情。你可能没少看有关《为什么习惯记笔记的人更容易成功》《学霸的笔记都是这么做的,难怪他们的考试分数那么高!》等文章。

    论文笔记的作用不仅仅是起到一种心理安慰,更重要的是划出重点、标记出知识盲区,便于后续温习。德国心理学家艾宾浩斯研究发现,人类在学习后就会开始遗忘,遗忘的程度是不均匀的,刚开始记忆下降比例很高,后面会越来越少。

    根据实验结果发现刚记住的时候是100%,过了20分钟记忆程度只有58.2%,2天和6天后分别只能记得27.8%和25.4%。

    如果只是单纯的看论文,很难将里面的知识点据为己有,这个时候就需要笔记来帮忙来延缓知识的流失速度了。

    我们经常看到网上动不动就最全XX论文全集,上来就是几十篇论文。人类都有囤积物资的欲求,囤积论文也差不多。我们往往觉得,我手中的论文数量越多,带给我的安全感就越大、我能收获的知识就越多。虽然理智告诉我们:这样想法是错误的!然而欲望却拖着我们像个过冬的松鼠一样,不断的收集松果。

    但如果硬是强迫自己看一篇论文就必须要做多少多少笔记,也不太现实。而且如果一开始没有培养期做笔记习惯的话,很可能一开始并没有get到做笔记的精髓,导致事无巨细全都记笔记。这个时候,看看别人做的笔记,尤其是看看学霸做的笔记,也是一个非常不错、极具实操性的方法。

    今天新智元为大家带来一位博士生的论文笔记。这位学霸真的是有很认真的看了不少论文,为了便于检索,他在看过的ML相关的论文进行了注释和简短摘要,并且将这些摘要按主题分类。

    他将论文和笔记都放在了GitHub上,非常方便进行对照。以下就是论文和笔记的列表,大家可以根据需要下载阅读。

    Self-Supervised Learning

    论文标题:Selfie: Self-supervised Pretraining for Image Embedding (2019)

    论文链接:

    https://arxiv.org/abs/1906.02940

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/76_selfie_pretraining_for_img_embeddings.pdf

    论文标题:Self-Supervised Representation Learning by Rotation Feature Decoupling (2019)

    论文链接:

    https://github.com/philiptheother/FeatureDecoupling

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/73_SSL_by_rotation_decoupling.pdf

    论文标题:Revisiting Self-Supervised Visual Representation Learning (2019)

    论文链接:

    https://arxiv.org/abs/1901.09005

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/72_revisiting_SSL.pdf

    论文标题:AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data (2019)

    论文链接:

    https://arxiv.org/abs/1901.04596

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/74_AFT_vs_AED.pdf

    论文标题:Boosting Self-Supervised Learning via Knowledge Transfer (2018)

    论文链接:

    https://arxiv.org/abs/1805.00385

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/67_boosting_self_super_via_trsf_learning.pdf

    论文标题:Self-Supervised Feature Learning by Learning to Spot Artifacts (2018)

    论文链接:

    https://arxiv.org/abs/1806.05024

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/69_SSL_by_learn_to_spot_artifacts.pdf

    论文标题:Unsupervised Representation Learning by Predicting Image Rotations (2018)

    论文链接:

    https://arxiv.org/abs/1803.07728

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/68_unsup_img_rep_learn_by_rot_predic.pdf

    论文标题:Cross Pixel Optical-Flow Similarity for Self-Supervised Learning (2018)

    论文链接:

    https://arxiv.org/abs/1807.05636

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/75_cross_pixel_optical_flow.pdf

    论文标题:Multi-task Self-Supervised Visual Learning (2017)

    论文链接:

    https://arxiv.org/abs/1708.07860

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/64_multi_task_self_supervised.pdf

    论文标题:Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction (2017)

    论文链接:

    https://arxiv.org/abs/1611.09842

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/65_split_brain_autoencoders.pdf

    论文标题:Colorization as a Proxy Task for Visual Understanding (2017)

    论文链接:

    https://arxiv.org/abs/1703.04044

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/66_colorization_as_a_proxy_for_viz_under.pdf

    论文标题:Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles (2017)

    论文链接:

    https://arxiv.org/abs/1603.09246

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/63_solving_jigsaw_puzzles.pdf

    论文标题:Unsupervised Visual Representation Learning by Context Prediction (2016)

    论文链接:

    https://arxiv.org/abs/1505.05192

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/62_unsupervised_learning_with_context_prediction.pdf

    论文标题:Colorful image colorization (2016)

    论文链接:

    https://richzhang.github.io/colorization/

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/59_colorful_colorization.pdf

    论文标题:Learning visual groups from co-occurrences in space and time (2015)

    论文链接:

    https://arxiv.org/abs/1511.06811

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/61_visual_groups_from_co_occurrences.pdf

    论文标题:Discriminative unsupervised feature learning with exemplar convolutional neural networks (2015)

    论文链接:

    https://arxiv.org/abs/1406.6909

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/60_exemplar_CNNs.pdf

    Semi-Supervised Learning

    论文标题:Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning (2019)

    论文链接:

    https://arxiv.org/abs/1909.01804

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/79_dual_student.pdf

    论文标题:S4L: Self-Supervised Semi-Supervised Learning (2019)

    论文链接:

    https://arxiv.org/abs/1905.03670

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/83_S4L.pdf

    论文标题:Semi-Supervised Learning by Augmented Distribution Alignment (2019)

    论文链接:

    https://arxiv.org/abs/1905.08171

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/80_SSL_aug_dist_align.pdf

    论文标题:MixMatch: A Holistic Approach toSemi-Supervised Learning (2019)

    论文链接:

    https://arxiv.org/abs/1905.02249

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/45_mixmatch.pdf

    论文标题:Unsupervised Data Augmentation (2019)

    论文链接:

    https://arxiv.org/abs/1904.12848

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/39_unsupervised_data_aug.pdf

    论文标题:Interpolation Consistency Training forSemi-Supervised Learning (2019)

    论文链接:

    https://arxiv.org/abs/1903.03825

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/44_interpolation_consistency_tranining.pdf

    论文标题:Deep Co-Training for Semi-Supervised Image Recognition (2018)

    论文链接:

    https://arxiv.org/abs/1803.05984

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/46_deep_co_training_img_rec.pdf

    论文标题:Unifying semi-supervised and robust learning by mixup (2019)

    论文链接:

    https://openreview.net/forum?id=r1gp1jRN_4

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/42_mixmixup.pdf

    论文标题:Realistic Evaluation of Deep Semi-Supervised Learning Algorithms (2018)

    论文链接:

    https://arxiv.org/abs/1804.09170

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/37_realistic_eval_of_deep_ss.pdf

    论文标题:Semi-Supervised Sequence Modeling with Cross-View Training (2018)

    论文链接:

    https://arxiv.org/abs/1809.08370

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/38_cross_view_semi_supervised.pdf

    论文标题:Virtual Adversarial Training:A Regularization Method for Supervised andSemi-Supervised Learning (2017)

    论文链接:

    https://arxiv.org/abs/1704.03976

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/40_virtual_adversarial_training.pdf

    论文标题:Mean teachers are better role models (2017)

    论文链接:

    https://arxiv.org/abs/1703.01780

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/56_mean_teachers.pdf

    论文标题:Temporal Ensembling for Semi-Supervised Learning (2017)

    论文链接:

    https://arxiv.org/abs/1610.02242

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/55_temporal-ensambling.pdf

    论文标题:Semi-Supervised Learning with Ladder Networks (2015)

    论文链接:

    https://arxiv.org/abs/1507.02672

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/33_ladder_nets.pdf

    Unsupervised Learning

    论文标题:Invariant Information Clustering for Unsupervised Image Classification and Segmentation (2019)

    论文链接:

    https://arxiv.org/abs/1807.06653

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/78_IIC.pdf

    论文标题:Deep Clustering for Unsupervised Learning of Visual Feature (2018)

    论文链接:

    https://arxiv.org/abs/1807.05520

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/70_deep_clustering_for_un_visual_features.pdf

    Semantic Segmentation

    论文标题:DeepLabv3+: Encoder-Decoder with Atrous Separable Convolution (2018)

    论文链接:

    https://arxiv.org/abs/1802.02611

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/26_deeplabv3+.pdf

    论文标题:Large Kernel Matter, Improve Semantic Segmentation by Global Convolutional Network (2017)

    论文链接:

    https://arxiv.org/abs/1703.02719

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/28_large_kernel_maters.pdf

    论文标题:Understanding Convolution for Semantic Segmentation (2018)

    论文链接:

    https://arxiv.org/abs/1702.08502

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/29_understanding_conv_for_sem_seg.pdf

    论文标题:Rethinking Atrous Convolution for Semantic Image Segmentation (2017)

    论文链接:

    https://arxiv.org/abs/1706.05587

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/25_deeplab_v3.pdf

    论文标题:RefineNet: Multi-path refinement networks for high-resolution semantic segmentation (2017)

    论文链接:

    https://arxiv.org/abs/1611.06612

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/31_refinenet.pdf

    论文标题:Pyramid Scene Parsing Network (2017)

    论文链接:

    http://jiaya.me/papers/PSPNet_cvpr17.pdf

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/22_pspnet.pdf

    论文标题:SegNet: A Deep ConvolutionalEncoder-Decoder Architecture for ImageSegmentation (2016)

    论文链接:

    https://arxiv.org/pdf/1511.00561

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/21_segnet.pdf

    论文标题:ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation (2016)

    论文链接:

    https://arxiv.org/abs/1606.02147

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/27_enet.pdf

    论文标题:Attention to Scale: Scale-aware Semantic Image Segmentation (2016)

    论文链接:

    https://arxiv.org/abs/1511.03339

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/30_atttention_to_scale.pdf

    论文标题:Deeplab: semantic image segmentation with DCNN, atrous convs and CRFs (2016)

    论文链接:

    https://arxiv.org/abs/1606.00915

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/23_deeplab_v2.pdf

    论文标题:U-Net: Convolutional Networks for Biomedical Image Segmentation (2015)

    论文链接:

    https://arxiv.org/abs/1505.04597

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/20_Unet.pdf

    论文标题:Fully Convolutional Networks for Semantic Segmentation (2015)

    论文链接:

    https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/19_FCN.pdf

    论文标题:Hypercolumns for object segmentation and fine-grained localization (2015)

    论文链接:

    http://home.bharathh.info/pubs/pdfs/BharathCVPR2015.pdf

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/24_hypercolumns.pdf

    Weakly

    论文标题:Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation (2019)

    论文链接:

    http://arxiv.org/abs/1904.11693

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/54_boxe_driven_weakly_segmentation.pdf

    论文标题:FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference (2019)

    论文链接:

    https://arxiv.org/abs/1902.10421

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/49_ficklenet.pdf

    论文标题:Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (2018)

    论文链接:

    http://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Weakly-Supervised_Semantic_Segmentation_CVPR_2018_paper.pdf

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/53_deep_seeded_region_growing.pdf

    论文标题:Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation (2018)

    论文链接:

    https://arxiv.org/abs/1803.10464

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/81_affinity_for_ws_segmentation.pdf

    论文标题:Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach (2018)

    论文链接:

    https://arxiv.org/abs/1703.08448

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/51_object_region_manning_for_sem_seg.pdf

    论文标题:Revisiting Dilated Convolution: A Simple Approach for Weakly

    论文标题:and Semi

    论文标题:Supervised Semantic Segmentation (2018)

    论文链接:

    https://arxiv.org/abs/1805.04574

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/52_dilates_convolution_semi_super_segmentation.pdf

    论文标题:Tell Me Where to Look: Guided Attention Inference Network (2018)

    论文链接:

    https://arxiv.org/abs/1802.10171

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/50_tell_me_where_to_look.pdf

    论文标题:Semi Supervised Semantic Segmentation Using Generative Adversarial Network (2017)

    论文链接:

    https://arxiv.org/abs/1703.09695

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/82_ss_segmentation_gans.pdf

    论文标题:Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation (2015)

    论文链接:

    https://arxiv.org/abs/1506.04924

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/47_decoupled_nn_for_segmentation.pdf

    论文标题:Weakly

    论文标题:and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation (2015)

    论文链接:

    https://arxiv.org/abs/1502.02734

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/48_weakly_and_ss_for_segmentation.pdf

    Information Retrieval

    论文标题:VSE++: Improving Visual-Semantic Embeddings with Hard Negatives (2018)

    论文链接:

    https://arxiv.org/abs/1707.05612

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/77_vse++.pdf

    Visual Explanation & Attention

    论文标题:Attention Branch Network: Learning of Attention Mechanism for Visual Explanation (2019)

    论文链接:

    https://arxiv.org/abs/1812.10025

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/57_attention_branch_netwrok.pdf

    论文标题:Attention-based Dropout Layer for Weakly Supervised Object Localization (2019)

    论文链接:

    http://openaccess.thecvf.com/content_CVPR_2019/papers/Choe_Attention-Based_Dropout_Layer_for_Weakly_Supervised_Object_Localization_CVPR_2019_paper.pdf

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/58_attention_based_dropout.pdf

    论文标题:Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer (2016)

    论文链接:

    https://arxiv.org/abs/1612.03928

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/71_attention_transfer.pdf

    Graph neural network & Graph embeddings

    论文标题:Pixels to Graphs by Associative Embedding (2017)

    论文链接:

    https://arxiv.org/abs/1706.07365

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/36_pixels_to_graphs.pdf

    论文标题:Associative Embedding: End-to-End Learning forJoint Detection and Grouping (2017)

    论文链接:

    https://arxiv.org/abs/1611.05424

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/35_associative_emb.pdf

    论文标题:Interaction Networks for Learning about Objects , Relations and Physics (2016)

    论文链接:

    https://arxiv.org/abs/1612.00222

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/18_interaction_nets.pdf

    论文标题:DeepWalk: Online Learning of Social Representation (2014)

    论文链接:

    http://www.perozzi.net/publications/14_kdd_deepwalk.pdf

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/deep_walk.pdf

    论文标题:The graph neural network model (2009)

    论文链接:

    https://persagen.com/files/misc/scarselli2009graph.pdf

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/graph_neural_nets.pdf

    Regularization

    论文标题:Manifold Mixup: Better Representations by Interpolating Hidden States (2018)

    论文链接:

    https://arxiv.org/abs/1806.05236

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/43_manifold_mixup.pdf

    Deep learning Methods & Models

    论文标题:AutoAugment (2018)

    论文链接:

    https://arxiv.org/abs/1805.09501

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/41_autoaugment.pdf

    论文标题:Stacked Hourgloass (2017)

    论文链接:

    http://ismir2018.ircam.fr/doc/pdfs/138_Paper.pdf

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/34_stacked_hourglass.pdf

    Document analysis and segmentation

    论文标题:dhSegment: A generic deep-learning approach for document segmentation (2018)

    论文链接:

    https://arxiv.org/abs/1804.10371

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/dhSegement.pdf

    论文标题:Learning to extract semantic structure from documents using multimodal fully convolutional neural networks (2017)

    论文链接:

    https://arxiv.org/abs/1706.02337

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/learning_to_extract.pdf

    论文标题:Page Segmentation for Historical Handwritten Document Images Using Conditional Random Fields (2016)

    论文链接:

    https://www.researchgate.net/publication/312486501_Page_Segmentation_for_Historical_Handwritten_Document_Images_Using_Conditional_Random_Fields

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/seg_with_CRFs.pdf

    论文标题:ICDAR 2015 competition on text line detection in historical documents (2015)

    论文链接:

    http://ieeexplore.ieee.org/abstract/document/7333945/

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/ICDAR2015.pdf

    论文标题:Handwritten text line segmentation using Fully Convolutional Network (2017)

    论文链接:

    https://ieeexplore.ieee.org/document/8270267/

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/handwritten_text_seg_FCN.pdf

    论文标题:Deep Neural Networks for Large Vocabulary Handwritten Text Recognition (2015)

    论文链接:

    https://tel.archives-ouvertes.fr/tel-01249405/document

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/andwriten_text_recognition.pdf

    论文标题:Page Segmentation of Historical Document Images with Convolutional Autoencoders (2015)

    论文链接:

    https://ieeexplore.ieee.org/abstract/document/7333914/

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/segmentation_with_CAE.pdf

    论文标题:A typed and handwritten text block segmentation system for heterogeneous and complex documents (2012)

    论文链接:

    https://www.researchgate.net/publication/275518176_A_Typed_and_Handwritten_Text_Block_Segmentation_System_for_Heterogeneous_and_Complex_Documents

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/a_typed_block_seg.pdf

    论文标题:Document layout analysis, Classical approaches (1992:2001)

    论文链接:

    https://pdfs.semanticscholar.org/5392/90b571b918da959fabaae7f605bb07850518.pdf

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/old_classical_approaches.pdf

    论文标题:Page Segmentation for Historical Document Images Based on Superpixel Classification with Unsupervised Feature Learning (2016)

    论文链接:

    https://ieeexplore.ieee.org/document/7490134

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/seg_with_superpixels.pdf

    论文标题:Paragraph text segmentation into lines with Recurrent Neural Networks (2015)

    论文链接:

    http://ieeexplore.ieee.org/abstract/document/7333803/

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/textlines_srg_with_RNNs.pdf

    论文标题:A comprehensive survey of mostly textual document segmentation algorithms since 2008 (2017 )

    论文链接:

    https://hal.archives-ouvertes.fr/hal-01388088/document

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/survey_doc_segmentation.pdf

    论文标题:Convolutional Neural Networks for Page Segmentation of Historical Document Images (2017)

    论文链接:

    https://arxiv.org/abs/1704.01474

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/CNNs_chen.pdf

    论文标题:ICDAR2009 Page Segmentation Competition (2009)

    论文链接:

    https://ieeexplore.ieee.org/document/5277763

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/ICDAR2009.pdf

    论文标题:Amethod for combining complementary techniques for document image segmentation (2009)

    论文链接:

    https://www.researchgate.net/publication/220600948_A_method_for_combining_complementary_techniques_for_document_image_segmentation

    笔记链接:

    https://github.com/yassouali/ML_paper_notes/blob/master/notes/a_method_for_combining_complementary_techniques.pdf

    完整列表:

    https://github.com/yassouali/ML_paper_notes

    - END -

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    展开全文
  • 记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文
  • 最新的机器学习相关的SCI论文,SCI高质量期刊论文英文原文,pdf格式,26篇,与机器学习相关的,2020年最新发布
  • 这篇论文是关于机器学习中的相关算法 并通过举例更进一步说明其中的算法
  • 原文地址: 计算机视觉、机器学习相关领域论文和源代码大集合 原文地址: 计算机视觉、机器学习相关领域论文和源代码大集合
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  • 顶] 计算机视觉、机器学习相关领域论文和源代码大集合--持续更新…… 计算机视觉、机器学习相关领域论文和源代码大集合--持续更新…… zouxy09@qq.com http://blog.csdn.net/zouxy09  注:下面有project网站的大...

    http://blog.csdn.net/zouxy09/article/details/8550952 

    顶] 计算机视觉、机器学习相关领域论文和源代码大集合--持续更新……

    计算机视觉、机器学习相关领域论文和源代码大集合--持续更新……

    zouxy09@qq.com http://blog.csdn.net/zouxy09 

    注:下面有project网站的大部分都有paper和相应的codeCode一般是C/C++或者Matlab代码。最近一次更新:2013-3-17

     一、特征提取Feature Extraction:

    ·         SIFT [1] [Demo program][SIFT Library] [VLFeat]

    ·         PCA-SIFT [2] [Project]

    ·         Affine-SIFT [3] [Project]

    ·         SURF [4] [OpenSURF] [Matlab Wrapper]

    ·         Affine Covariant Features [5] [Oxford project]

    ·         MSER [6] [Oxford project] [VLFeat]

    ·         Geometric Blur [7] [Code]

    ·         Local Self-Similarity Descriptor [8] [Oxford implementation]

    ·         Global and Efficient Self-Similarity [9] [Code]

    ·         Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows]

    ·         GIST [11] [Project]

    ·         Shape Context [12] [Project]

    ·         Color Descriptor [13] [Project]

    ·         Pyramids of Histograms of Oriented Gradients [Code]

    ·         Space-Time Interest Points (STIP) [14][Project] [Code]

    ·         Boundary Preserving Dense Local Regions [15][Project]

    ·         Weighted Histogram[Code]

    ·         Histogram-based Interest Points Detectors[Paper][Code]

    ·         An OpenCV - C++ implementation of Local Self Similarity Descriptors [Project]

    ·         Fast Sparse Representation with Prototypes[Project]

    ·         Corner Detection [Project]

    ·         AGAST Corner Detector: faster than FAST and even FAST-ER[Project]

    ·         Real-time Facial Feature Detection using Conditional Regression Forests[Project]

    ·         Global and Efficient Self-Similarity for Object Classification and Detection[code]

    ·         WαSH: Weighted α-Shapes for Local Feature Detection[Project]

    ·         HOG[Project]

    ·         Online Selection of Discriminative Tracking Features[Project]

                            

    二、图像分割Image Segmentation:

    ·           Normalized Cut [1] [Matlab code]

    ·           Gerg Mori’ Superpixel code [2] [Matlab code]

    ·           Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]

    ·           Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]

    ·           OWT-UCM Hierarchical Segmentation [5] [Resources]

    ·           Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]

    ·           Quick-Shift [7] [VLFeat]

    ·           SLIC Superpixels [8] [Project]

    ·           Segmentation by Minimum Code Length [9] [Project]

    ·           Biased Normalized Cut [10] [Project]

    ·           Segmentation Tree [11-12] [Project]

    ·           Entropy Rate Superpixel Segmentation [13] [Code]

    ·           Fast Approximate Energy Minimization via Graph Cuts[Paper][Code]

    ·           Efficient Planar Graph Cuts with Applications in Computer Vision[Paper][Code]

    ·           Isoperimetric Graph Partitioning for Image Segmentation[Paper][Code]

    ·           Random Walks for Image Segmentation[Paper][Code]

    ·           Blossom V: A new implementation of a minimum cost perfect matching algorithm[Code]

    ·           An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[Paper][Code]

    ·           Geodesic Star Convexity for Interactive Image Segmentation[Project]

    ·           Contour Detection and Image Segmentation Resources[Project][Code]

    ·           Biased Normalized Cuts[Project]

    ·           Max-flow/min-cut[Project]

    ·           Chan-Vese Segmentation using Level Set[Project]

    ·           A Toolbox of Level Set Methods[Project]

    ·           Re-initialization Free Level Set Evolution via Reaction Diffusion[Project]

    ·           Improved C-V active contour model[Paper][Code]

    ·           A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[Paper][Code]

    ·          Level Set Method Research by Chunming Li[Project]

    ·          ClassCut for Unsupervised Class Segmentation[code]

    ·         SEEDS: Superpixels Extracted via Energy-Driven Sampling [Project][other]

     

    三、目标检测Object Detection:

    ·           A simple object detector with boosting [Project]

    ·           INRIA Object Detection and Localization Toolkit [1] [Project]

    ·           Discriminatively Trained Deformable Part Models [2] [Project]

    ·           Cascade Object Detection with Deformable Part Models [3] [Project]

    ·           Poselet [4] [Project]

    ·           Implicit Shape Model [5] [Project]

    ·           Viola and Jones’s Face Detection [6] [Project]

    ·           Bayesian Modelling of Dyanmic Scenes for Object Detection[Paper][Code]

    ·           Hand detection using multiple proposals[Project]

    ·           Color Constancy, Intrinsic Images, and Shape Estimation[Paper][Code]

    ·           Discriminatively trained deformable part models[Project]

    ·           Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD [Project]

    ·           Image Processing On Line[Project]

    ·           Robust Optical Flow Estimation[Project]

    ·           Where's Waldo: Matching People in Images of Crowds[Project]

    ·           Scalable Multi-class Object Detection[Project]

    ·           Class-Specific Hough Forests for Object Detection[Project]

    ·         Deformed Lattice Detection In Real-World Images[Project]

    ·         Discriminatively trained deformable part models[Project]

     

    四、显著性检测Saliency Detection:

    ·           Itti, Koch, and Niebur’ saliency detection [1] [Matlab code]

    ·           Frequency-tuned salient region detection [2] [Project]

    ·           Saliency detection using maximum symmetric surround [3] [Project]

    ·           Attention via Information Maximization [4] [Matlab code]

    ·           Context-aware saliency detection [5] [Matlab code]

    ·           Graph-based visual saliency [6] [Matlab code]

    ·           Saliency detection: A spectral residual approach. [7] [Matlab code]

    ·           Segmenting salient objects from images and videos. [8] [Matlab code]

    ·           Saliency Using Natural statistics. [9] [Matlab code]

    ·           Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code]

    ·           Learning to Predict Where Humans Look [11] [Project]

    ·           Global Contrast based Salient Region Detection [12] [Project]

    ·           Bayesian Saliency via Low and Mid Level Cues[Project]

    ·           Top-Down Visual Saliency via Joint CRF and Dictionary Learning[Paper][Code]

    ·         Saliency Detection: A Spectral Residual Approach[Code]

     

    五、图像分类、聚类Image Classification, Clustering

    ·           Pyramid Match [1] [Project]

    ·           Spatial Pyramid Matching [2] [Code]

    ·           Locality-constrained Linear Coding [3] [Project] [Matlab code]

    ·           Sparse Coding [4] [Project] [Matlab code]

    ·           Texture Classification [5] [Project]

    ·           Multiple Kernels for Image Classification [6] [Project]

    ·           Feature Combination [7] [Project]

    ·           SuperParsing [Code]

    ·           Large Scale Correlation Clustering Optimization[Matlab code]

    ·           Detecting and Sketching the Common[Project]

    ·           Self-Tuning Spectral Clustering[Project][Code]

    ·           User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior[Paper][Code]

    ·           Filters for Texture Classification[Project]

    ·           Multiple Kernel Learning for Image Classification[Project]

    ·          SLIC Superpixels[Project]

     

    六、抠图Image Matting

    ·           A Closed Form Solution to Natural Image Matting [Code]

    ·           Spectral Matting [Project]

    ·           Learning-based Matting [Code]

     

    七、目标跟踪Object Tracking:

    ·           A Forest of Sensors - Tracking Adaptive Background Mixture Models [Project]

    ·           Object Tracking via Partial Least Squares Analysis[Paper][Code]

    ·           Robust Object Tracking with Online Multiple Instance Learning[Paper][Code]

    ·           Online Visual Tracking with Histograms and Articulating Blocks[Project]

    ·           Incremental Learning for Robust Visual Tracking[Project]

    ·           Real-time Compressive Tracking[Project]

    ·           Robust Object Tracking via Sparsity-based Collaborative Model[Project]

    ·           Visual Tracking via Adaptive Structural Local Sparse Appearance Model[Project]

    ·           Online Discriminative Object Tracking with Local Sparse Representation[Paper][Code]

    ·           Superpixel Tracking[Project]

    ·           Learning Hierarchical Image Representation with Sparsity, Saliency and Locality[Paper][Code]

    ·           Online Multiple Support Instance Tracking [Paper][Code]

    ·           Visual Tracking with Online Multiple Instance Learning[Project]

    ·           Object detection and recognition[Project]

    ·           Compressive Sensing Resources[Project]

    ·           Robust Real-Time Visual Tracking using Pixel-Wise Posteriors[Project]

    ·           Tracking-Learning-Detection[Project][OpenTLD/C++ Code]

    ·           the HandVu:vision-based hand gesture interface[Project]

    ·           Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities[Project]

     

    八、Kinect:

    ·           Kinect toolbox[Project]

    ·           OpenNI[Project]

    ·           zouxy09 CSDN Blog[Resource]

    ·           FingerTracker 手指跟踪[code]

     

    九、3D相关:

    ·           3D Reconstruction of a Moving Object[Paper] [Code]

    ·           Shape From Shading Using Linear Approximation[Code]

    ·           Combining Shape from Shading and Stereo Depth Maps[Project][Code]

    ·           Shape from Shading: A Survey[Paper][Code]

    ·           A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)[Project][Code]

    ·           Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code]

    ·           A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination[Paper][Code]

    ·           Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture[Project]

    ·           Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers[Code]

    ·           Learning 3-D Scene Structure from a Single Still Image[Project]

     

    十、机器学习算法:

    ·           Matlab class for computing Approximate Nearest Nieghbor (ANN) [Matlab class providing interface toANN library]

    ·           Random Sampling[code]

    ·           Probabilistic Latent Semantic Analysis (pLSA)[Code]

    ·           FASTANN and FASTCLUSTER for approximate k-means (AKM)[Project]

    ·           Fast Intersection / Additive Kernel SVMs[Project]

    ·           SVM[Code]

    ·           Ensemble learning[Project]

    ·           Deep Learning[Net]

    ·           Deep Learning Methods for Vision[Project]

    ·           Neural Network for Recognition of Handwritten Digits[Project]

    ·           Training a deep autoencoder or a classifier on MNIST digits[Project]

    ·          THE MNIST DATABASE of handwritten digits[Project]

    ·          Ersatz:deep neural networks in the cloud[Project]

    ·          Deep Learning [Project]

    ·          sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++[Project]

    ·          Weka 3: Data Mining Software in Java[Project]

    ·          Invited talk "A Tutorial on Deep Learning" by Dr. Kai Yu (余凯)[Video]

    ·          CNN - Convolutional neural network class[Matlab Tool]

    ·          Yann LeCun's Publications[Wedsite]

    ·          LeNet-5, convolutional neural networks[Project]

    ·          Training a deep autoencoder or a classifier on MNIST digits[Project]

    ·          Deep Learning 大牛Geoffrey E. Hinton's HomePage[Website]

    ·         Multiple Instance Logistic Discriminant-based Metric Learning (MildML) and Logistic Discriminant-based Metric Learning (LDML)[Code]

    ·         Sparse coding simulation software[Project]

    ·         Visual Recognition and Machine Learning Summer School[Software]

     

    十一、目标、行为识别Object, Action Recognition:

    ·           Action Recognition by Dense Trajectories[Project][Code]

    ·           Action Recognition Using a Distributed Representation of Pose and Appearance[Project]

    ·           Recognition Using Regions[Paper][Code]

    ·           2D Articulated Human Pose Estimation[Project]

    ·           Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[Paper][Code]

    ·           Estimating Human Pose from Occluded Images[Paper][Code]

    ·           Quasi-dense wide baseline matching[Project]

    ·           ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[Project]

    ·           Real Time Head Pose Estimation with Random Regression Forests[Project]

    ·           2D Action Recognition Serves 3D Human Pose Estimation[Project]

    ·           A Hough Transform-Based Voting Framework for Action Recognition[Project]

    ·           Motion Interchange Patterns for Action Recognition in Unconstrained Videos[Project]

    ·         2D articulated human pose estimation software[Project]

    ·         Learning and detecting shape models [code]

    ·         Progressive Search Space Reduction for Human Pose Estimation[Project]

    ·         Learning Non-Rigid 3D Shape from 2D Motion[Project]

     

    十二、图像处理:

    ·         Distance Transforms of Sampled Functions[Project]

    ·         The Computer Vision Homepage[Project]

    ·         Efficient appearance distances between windows[code]

    ·         Image Exploration algorithm[code]

    ·         Motion Magnification 运动放大 [Project]

    ·         Bilateral Filtering for Gray and Color Images 双边滤波器 [Project]

    ·         A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [Project]

                      

    十三、一些实用工具:

    ·           EGT: a Toolbox for Multiple View Geometry and Visual Servoing[Project] [Code]

    ·           a development kit of matlab mex functions for OpenCV library[Project]

    ·           Fast Artificial Neural Network Library[Project]

     

    十四、人手及指尖检测与识别:

      finger-detection-and-gesture-recognition [Code]

      Hand and Finger Detection using JavaCV[Project]

      Hand and fingers detection[Code]

     

    十五、场景解释:

       Nonparametric Scene Parsing via Label Transfer [Project]

     

    十六、光流Optical flow:

      High accuracy optical flow using a theory for warping [Project]

      Dense Trajectories Video Description [Project]

      SIFT Flow: Dense Correspondence across Scenes and its Applications[Project]

      KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker [Project]

       Tracking Cars Using Optical Flow[Project]

      Secrets of optical flow estimation and their principles[Project]

    ·         implmentation of the Black and Anandan dense optical flow method[Project]

    ·         Optical Flow Computation[Project]

    ·         Beyond Pixels: Exploring New Representations and Applications for Motion Analysis[Project]

    ·         A Database and Evaluation Methodology for Optical Flow[Project]

    ·         optical flow relative[Project]

    ·         Robust Optical Flow Estimation [Project]

    ·         optical flow[Project]


    十七、图像检索Image Retrieval

       Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval [Paper][code]


     

    十八、马尔科夫随机场Markov Random Fields:

       Markov Random Fields for Super-Resolution [Project]

       A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors [Project]


    十九、运动检测Motion detection:

    ·         Moving Object Extraction, Using Models or Analysis of Regions [Project]

    ·         Background Subtraction: Experiments and Improvements for ViBe [Project]

    ·         A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications [Project]

    ·         changedetection.net: A new change detection benchmark dataset[Project]

    ·         ViBe - a powerful technique for background detection and subtraction in video sequences[Project]

    ·         Background Subtraction Program[Project]

    ·         Motion Detection Algorithms[Project]

    ·         Stuttgart Artificial Background Subtraction Dataset[Project]

    ·         Object Detection, Motion Estimation, and Tracking[Project]

    ============================================================================================
    下述信息来源于中科院自动化所网站
    http://www.sigvc.org/why/resource.htm

    People (Currently related to me, but NOT ALL, ref. [Hongbo Fu's link]):

    Current:


    项目符号 Hao Li
    项目符号 Michael Wand
    项目符号 Daniel Vlasic
    项目符号 Cedric Cagniart
    项目符号 Juergen Gall
    项目符号 Jonathan Starck
    项目符号 Andriy Myronenko
    项目符号 Radu Patrice HORAUD
    项目符号 Dragomir Anguelov (Drago)
    项目符号 Qi-xing Huang
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     US:

    项目符号 Raif M. Rustamov
    项目符号 Ronald Fedkiw,
    项目符号 Leo Guibas,
    项目符号 Pat Hanrahan,
    项目符号 Marc Levoy@Stanford,
    项目符号 Zoran Popović, (运动捕捉)
    项目符号 Steven M. Seitz,
    项目符号 Brian Curless,
    项目符号 David H. Salesin@Washington,
    项目符号 Sara McMains,
    项目符号 David A. Forsyth,
    项目符号 James F. O'Brien,
    项目符号 Brian A. Barsky,
    项目符号 Carlo H. Séquin,
    项目符号 Jonathan Shewchuk,
    项目符号 Martin Isenburg,
    项目符号 Maneesh Agrawala@Berkeley,
    项目符号 Hugues Hoppe, (网格渐进压缩)
    项目符号 Ross T. Whitaker
    项目符号 Charles Loop,
    项目符号 Jim Blinn,
    项目符号 Michael Cohen,
    项目符号 Richard Szeliski@Microsoft,
    项目符号 Levent Burak Kara,
    项目符号 Kenji Shimada,
    项目符号 Jessica K. Hodgins@CMU,
    项目符号 Ken Perlin,
    项目符号 Denis Zorin@NYU,
    项目符号 Alan H. Barr,
    项目符号 Mathieu Desbrun, (微分几何)
    项目符号 Peter Schröder,
    项目符号 Yiying Tong@CalTech, (DEC)
    项目符号 Lexing Ying@UTexas,
    项目符号 Frédo Durand (Bookmarks),
    项目符号 Anat Levin,
    项目符号 Sylvain Paris,
    项目符号 Jovan Popović@MIT, (动画)
    项目符号 Anil N. Hirani, (DEC)
    项目符号 Yizhou Yu, (形变)
    项目符号 Michael Garland, (网格简化、处理)
    项目符号 John C. Hart@UIUC,
    项目符号 Eitan Grinspun,
    项目符号 Shree K. Nayar, (视觉图形渲染)
    项目符号 Ravi Ramamoorthi@Columbia,
    项目符号 Szymon Rusinkiewicz,
    项目符号 Adam Finkelstein,
    项目符号 Thomas A. Funkhouser@Princeton,
    项目符号 Steven J. Gortler@Harvard,
    项目符号 Henry Fuchs,
    项目符号 Dinesh Manocha UNC,
    项目符号 Ming C. Lin@UNC,
    项目符号 Ken Joy,
    项目符号 Bernd Hamann,
    项目符号 Nina Amenta,
    项目符号 Kwan-Liu Ma@UC Davis,
    项目符号 Arie Kaufman,
    项目符号 Xianfeng Gu,(几何处理,Conformal Structure ,General Purpose Mesh Library)
    项目符号 Hong Qin@SunySB, (几何处理)
    项目符号 Henrik Wann Jensen,
    项目符号 Matthias Zwicker@UCSD,
    项目符号 Doug DeCarlo@Rutgers,
    项目符号 David H. Laidlaw,
    项目符号 Andy van Dam,
    项目符号 Gabriel Taubin,
    项目符号 John F. Hughes@Brown,
    项目符号 Scott Schaefer,
    项目符号 Ron Goldman,
    项目符号 Joe Warren@Rice,
    项目符号 Jorg Peters@UFL,
    项目符号 Irfan Essa,
    项目符号 Peter J. Mucha,
    项目符号 Jarek Rossignac,
    项目符号 Greg Turk@GaTech,
    项目符号 Jonathan D. Cohen,
    项目符号 Michael Kazhdan@JHU,
    项目符号 Cindy Grimm, (参数化)
    项目符号 Tao Ju@WUSTL, (几何处理)
    项目符号 Ilya Eckstein(几何流,变分)
    项目符号 Claudio Silva@Utah,
    项目符号 Tamal K. Dey@Ohio State,
    项目符号 Wojciech Matusik,
    项目符号 Ramesh Raskar,
    项目符号 Hanspeter Pfister@Merl,
    项目符号 Jos Stam@Autodesk,
    项目符号 Ulrich Neumann,
    项目符号 Paul Debevec@USC,
    项目符号 Karuhiro Saitou,
    项目符号 Lee Markosian,
    项目符号 Igor Guskov@Umich, (DGP)
    项目符号 Normann I. Badler @Upenn,
    项目符号 Valerio Pascucci,
    项目符号 Peter Lindstrom@LLNL,
    项目符号 Fuhua (Frank) Cheng@Kentucky,
    项目符号 Baoquan Chen@UMN,
    项目符号 Ioana Boier-Martin@IBM,
    项目符号 Herbert Edelsbrunner@Duke,
    项目符号 Andrei Khodakovsky@nVidia,
    项目符号 Vadim Shapiro,
    项目符号 Michael Gleicher@Wisconsin,
    项目符号 David Ebert @Purdue,
    项目符号 Gopi Meenakshisundaram@UCI,
    项目符号 Tomas Sederberg@Brigham Young Univ.,
    项目符号 Doug L. James@Cornell,
    项目符号 Petros Faloutsos,
    项目符号 Demetri Terzopoulos@UCLA,
    项目符号 Chandrajit Bajaj,
    项目符号 Okan Arikan@Texas,
    项目符号 Amitabh Varshney@Maryland,
    项目符号 Julie Dorsey@Yale,
    项目符号 Rui Wang@UMass
    项目符号 Carsten Rother(图像纹理)
    项目符号 Haibin Ling(视觉图像)
    项目符号 John Schreiner (参数化、remesh;surface triangulation源码)
    项目符号 Dr. Benedict J. Brown(几何匹配,文化遗产保护,源码和博士论文)
    项目符号 Song-Chun Zhu(视觉,统计,Markov Chain Monte Carlo for Computer Vision ICCV05教程)
    项目符号 Ross T. Whitaker(可视化、图像,视觉)
    项目符号 Zhengyou Zhang
    项目符号 Stéphane Mallat(小波)
    项目符号 Yingnian Wu(UCLA统计)
    项目符号 Tim Cootes(AAM,ASM)
    项目符号 David LoweSift
    项目符号 Jinxiang Chai
    项目符号 Michael M. Bronstein (CG & CV)
    项目符号 Jean-Yves Bouguet (标定,重建)

    Germany:

    项目符号 Alexander G. Belyaev,
    项目符号 Zachi Karni,
    项目符号 Rhaleb Zayer, (DGP)
    项目符号 Hans-Peter Seidel@MPI,
    项目符号 Leif Kobbelt@RWTH, (大组)
    项目符号 Olga Sorkine, (几何处理)
    项目符号 Marc Alexa@TU Berlin,
    项目符号 Konrad Polthier@FU Berlin,
    项目符号 Stefan Gumhold@TU dresden,
    项目符号 Hans-Christian Hege@ZIB,
    项目符号 Kai Hormann@TU Clausthal(参数化)
    项目符号 Volker Blanz(morphing model)
    项目符号 Holger Theisel (可视化、图形学结合)
    项目符号 Daniel Weiskopf (可视化)


    Canada:

    项目符号 Vladislav Kraevoy, (DGP)
    项目符号 Alla Sheffa,
    项目符号 Dinesh K. Pai@UBC,
    项目符号 Aaron Hertzmann,
    项目符号 Karan Singh,
    项目符号 Eugene Fiume@Toronto,
    项目符号 Hao Zhang@SFU, @Waterloo (谱分析,DGP课程)
    项目符号 Oliver Matias van Kaick(谱方法,压缩)
    项目符号 Martin Reuter (谱方法,MIT)

    UK:

    项目符号 Frank Langbein,
    项目符号 Ralph R. Martin@Cardiff,
    项目符号 Jiang J Zhang@Bournemouth,
    项目符号 Alan Chalmers@Bristol,
    项目符号 Neil Dodgson,
    项目符号 Peter Robinson@Cambridge
    项目符号 Vladimir Kolmogorov (graphcut,Markov Random Fields (MRFs),courses)
    项目符号 Edwin Hancock(图论统计视觉)
    项目符号 Christopher M. Bishop


    China Mainland:

    项目符号 Hongxin Zhang, (数学课程)
    项目符号 雍俊海
    项目符号 Ligang Liu, (刘利刚)
    项目符号 潘春洪
    项目符号 Xiaogang Jin,
    项目符号 Guoping Wang, 北大软件研究所
    项目符号 Xinguo Liu,
    项目符号 Wei Chen,
    项目符号 Jiaoying Shi,
    项目符号 Qunsheng Peng,
    项目符号 王文成
    项目符号 Hujun Bao@ZJU,
    项目符号 Shimin Hu@tsinghua,
    项目符号 雍俊海
    项目符号 Kun Zhou, 浙大blog
    项目符号 Xin Tong, (渲染)
    项目符号 Jian Sun,
    项目符号 Stephen Lin,
    项目符号 Baining Guo,
    项目符号 Heung-Yeung Shum@MSRA,
    项目符号 Hongbin Zha@PKU,
    项目符号 Falai Chen@USTC
    项目符号 陈宝权(深圳研究院,可视化,三维扫描)
    项目符号 郭延文(图像纹理)
    项目符号 章国锋(浙大,视频视觉处理)
    项目符号 Wei Chen(图像,可视化,有PDE和游戏的课程)
    项目符号 胡事民
    项目符号 Liefeng Bo(西电,机器学习,SVM代码)
    项目符号 Haifeng Gong
    项目符号 Gang ZENG
    项目符号 Yong Liu (蒋田仔医学图象)
    项目符号 刘青山
    项目符号 林倞

    Taiwan:

    项目符号 Tong-Yee Lee@ncku

    Hong Kong:

    项目符号 Chiew-Lan Tai,
    项目符号 Long Quan,
    项目符号 Chi-Keung Tang,
    项目符号 Huamin Qu,
    项目符号 Philip Fu,
    项目符号 Albert CHUNG,
    项目符号 Pedro Sander,
    项目符号 Kai Tang@HKUST,
    项目符号 Wenping Wang@HKU, (CAD)
    项目符号 Charlie C.L. Wang,
    项目符号 Jiaya Jia,
    项目符号 Tien-Tsin Wong, (渲染)
    项目符号 Heng Pheng-Ann@CUHK, ???@CityU,
    项目符号 BACIU Georgei@PolyU
    项目符号 Dr. Kenneth K.Y. Wong(视觉建模)
    项目符号 Hongbo Fu
    项目符号 Oscar Kin-Chung Au(形变建模)
    项目符号 Tai-Pang WU(图像视觉)
    项目符号 Horace Ip Ho-shing

    Macao:

    项目符号 Enhua Wu@UMac

    Israel:

    项目符号 Yaron Lipman, (微分几何建模)
    项目符号 Daniel Cohen-Or, (几何处理,大组)
    项目符号 David Levin@Tel Aviv,
    项目符号 Craig Gotsman,
    项目符号 Gill Barequet,
    项目符号 Gershon Elber,
    项目符号 Ron Kimmel,
    项目符号 Ayellet Tal@Technion,
    项目符号 Dani Lischinski@HUJI
    项目符号 Andrei Sharf(几何建模,修复)
    项目符号 Sagi Katz (网格分割)
    项目符号 Nir Sochen (CV Beltrami)

    Austria:

    项目符号 Helmut Pottmann, (几何)
    项目符号 Qixing Huang,
    项目符号 Michael Hofer,
    项目符号 Andreas Asperl,
    项目符号 Martin Peternell,
    项目符号 Johannes Wallner,
    项目符号 Werner Purgathofer@TU Wien,
    项目符号 Bert Jüttler@JKU
    项目符号 Dr. AJMAL S. MIAN(视觉建模)
    项目符号 Liang Wang
     

    Japan:

    项目符号 Tomoyuki Nishita,
    项目符号 Takashi Kanai,
    项目符号 Takeo Igarashi@Toyko, (sketch)
    项目符号 Yutaka Ohtake@RIKEN
    项目符号 Kenichi Kanatani (射影几何代码)
    项目符号 Andrew Nealen (sketch)

    Korea:

    项目符号 Myung-Soo Kim,
    项目符号 Hyeong-Seok Ko@Seoul,
    项目符号 Sung Yong Shin@KAIST
    项目符号 Yunjin Lee(分割)

    Singapore:

    项目符号 Michael Brown@NTU,
    项目符号 Tan Tiow Seng,
    项目符号 Zhiyong Huang,
    项目符号 Chionh Eng Wee@NUS
    项目符号 Shuicheng Yan(视觉机器学习)
    项目符号 谭平(视觉建模)



    项目符号 发Science paper的人:Josh Tenenbaum (MIT); Tomaso Poggio(MIT)




    展开全文
  • 纸机视觉:记录每天整理的计算机视觉深度学习机器学习相关方向的论文
  • 计算机视觉、机器学习相关领域论文和源代码
    一、特征提取Feature Extraction:

    ·         SIFT [1] [Demo program][SIFT Library] [VLFeat]

    ·         PCA-SIFT [2] [Project]

    ·         Affine-SIFT [3] [Project]

    ·         SURF [4] [OpenSURF] [Matlab Wrapper]

    ·         Affine Covariant Features [5] [Oxford project]

    ·         MSER [6] [Oxford project] [VLFeat]

    ·         Geometric Blur [7] [Code]

    ·         Local Self-Similarity Descriptor [8] [Oxford implementation]

    ·         Global and Efficient Self-Similarity [9] [Code]

    ·         Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows]

    ·         GIST [11] [Project]

    ·         Shape Context [12] [Project]

    ·         Color Descriptor [13] [Project]

    ·         Pyramids of Histograms of Oriented Gradients [Code]

    ·         Space-Time Interest Points (STIP) [14][Project] [Code]

    ·         Boundary Preserving Dense Local Regions [15][Project]

    ·         Weighted Histogram[Code]

    ·         Histogram-based Interest Points Detectors[Paper][Code]

    ·         An OpenCV - C++ implementation of Local Self Similarity Descriptors [Project]

    ·         Fast Sparse Representation with Prototypes[Project]

    ·         Corner Detection [Project]

    ·         AGAST Corner Detector: faster than FAST and even FAST-ER[Project]

    ·         Real-time Facial Feature Detection using Conditional Regression Forests[Project]

    ·         Global and Efficient Self-Similarity for Object Classification and Detection[code]

    ·         WαSH: Weighted α-Shapes for Local Feature Detection[Project]

    ·         HOG[Project]

    ·         Online Selection of Discriminative Tracking Features[Project]

                           

    二、图像分割Image Segmentation:

    ·           Normalized Cut [1] [Matlab code]

    ·           Gerg Mori’ Superpixel code [2] [Matlab code]

    ·           Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]

    ·           Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]

    ·           OWT-UCM Hierarchical Segmentation [5] [Resources]

    ·           Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]

    ·           Quick-Shift [7] [VLFeat]

    ·           SLIC Superpixels [8] [Project]

    ·           Segmentation by Minimum Code Length [9] [Project]

    ·           Biased Normalized Cut [10] [Project]

    ·           Segmentation Tree [11-12] [Project]

    ·           Entropy Rate Superpixel Segmentation [13] [Code]

    ·           Fast Approximate Energy Minimization via Graph Cuts[Paper][Code]

    ·           Efficient Planar Graph Cuts with Applications in Computer Vision[Paper][Code]

    ·           Isoperimetric Graph Partitioning for Image Segmentation[Paper][Code]

    ·           Random Walks for Image Segmentation[Paper][Code]

    ·           Blossom V: A new implementation of a minimum cost perfect matching algorithm[Code]

    ·           An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[Paper][Code]

    ·           Geodesic Star Convexity for Interactive Image Segmentation[Project]

    ·           Contour Detection and Image Segmentation Resources[Project][Code]

    ·           Biased Normalized Cuts[Project]

    ·           Max-flow/min-cut[Project]

    ·           Chan-Vese Segmentation using Level Set[Project]

    ·           A Toolbox of Level Set Methods[Project]

    ·           Re-initialization Free Level Set Evolution via Reaction Diffusion[Project]

    ·           Improved C-V active contour model[Paper][Code]

    ·           A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[Paper][Code]

    ·          Level Set Method Research by Chunming Li[Project]

    ·          ClassCut for Unsupervised Class Segmentation[code]

    ·         SEEDS: Superpixels Extracted via Energy-Driven Sampling [Project][other]

     

    三、目标检测Object Detection:

    ·           A simple object detector with boosting [Project]

    ·           INRIA Object Detection and Localization Toolkit [1] [Project]

    ·           Discriminatively Trained Deformable Part Models [2] [Project]

    ·           Cascade Object Detection with Deformable Part Models [3] [Project]

    ·           Poselet [4] [Project]

    ·           Implicit Shape Model [5] [Project]

    ·           Viola and Jones’s Face Detection [6] [Project]

    ·           Bayesian Modelling of Dyanmic Scenes for Object Detection[Paper][Code]

    ·           Hand detection using multiple proposals[Project]

    ·           Color Constancy, Intrinsic Images, and Shape Estimation[Paper][Code]

    ·           Discriminatively trained deformable part models[Project]

    ·           Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD [Project]

    ·           Image Processing On Line[Project]

    ·           Robust Optical Flow Estimation[Project]

    ·           Where's Waldo: Matching People in Images of Crowds[Project]

    ·           Scalable Multi-class Object Detection[Project]

    ·           Class-Specific Hough Forests for Object Detection[Project]

    ·         Deformed Lattice Detection In Real-World Images[Project]

    ·         Discriminatively trained deformable part models[Project]

     

    四、显著性检测Saliency Detection:

    ·           Itti, Koch, and Niebur’ saliency detection [1] [Matlab code]

    ·           Frequency-tuned salient region detection [2] [Project]

    ·           Saliency detection using maximum symmetric surround [3] [Project]

    ·           Attention via Information Maximization [4] [Matlab code]

    ·           Context-aware saliency detection [5] [Matlab code]

    ·           Graph-based visual saliency [6] [Matlab code]

    ·           Saliency detection: A spectral residual approach. [7] [Matlab code]

    ·           Segmenting salient objects from images and videos. [8] [Matlab code]

    ·           Saliency Using Natural statistics. [9] [Matlab code]

    ·           Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code]

    ·           Learning to Predict Where Humans Look [11] [Project]

    ·           Global Contrast based Salient Region Detection [12] [Project]

    ·           Bayesian Saliency via Low and Mid Level Cues[Project]

    ·           Top-Down Visual Saliency via Joint CRF and Dictionary Learning[Paper][Code]

    ·         Saliency Detection: A Spectral Residual Approach[Code]

     

    五、图像分类、聚类Image Classification, Clustering

    ·           Pyramid Match [1] [Project]

    ·           Spatial Pyramid Matching [2] [Code]

    ·           Locality-constrained Linear Coding [3] [Project] [Matlab code]

    ·           Sparse Coding [4] [Project] [Matlab code]

    ·           Texture Classification [5] [Project]

    ·           Multiple Kernels for Image Classification [6] [Project]

    ·           Feature Combination [7] [Project]

    ·           SuperParsing [Code]

    ·           Large Scale Correlation Clustering Optimization[Matlab code]

    ·           Detecting and Sketching the Common[Project]

    ·           Self-Tuning Spectral Clustering[Project][Code]

    ·           User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior[Paper][Code]

    ·           Filters for Texture Classification[Project]

    ·           Multiple Kernel Learning for Image Classification[Project]

    ·          SLIC Superpixels[Project]

     

    六、抠图Image Matting

    ·           A Closed Form Solution to Natural Image Matting [Code]

    ·           Spectral Matting [Project]

    ·           Learning-based Matting [Code]

     

    七、目标跟踪Object Tracking:

    ·           A Forest of Sensors - Tracking Adaptive Background Mixture Models [Project]

    ·           Object Tracking via Partial Least Squares Analysis[Paper][Code]

    ·           Robust Object Tracking with Online Multiple Instance Learning[Paper][Code]

    ·           Online Visual Tracking with Histograms and Articulating Blocks[Project]

    ·           Incremental Learning for Robust Visual Tracking[Project]

    ·           Real-time Compressive Tracking[Project]

    ·           Robust Object Tracking via Sparsity-based Collaborative Model[Project]

    ·           Visual Tracking via Adaptive Structural Local Sparse Appearance Model[Project]

    ·           Online Discriminative Object Tracking with Local Sparse Representation[Paper][Code]

    ·           Superpixel Tracking[Project]

    ·           Learning Hierarchical Image Representation with Sparsity, Saliency and Locality[Paper][Code]

    ·           Online Multiple Support Instance Tracking [Paper][Code]

    ·           Visual Tracking with Online Multiple Instance Learning[Project]

    ·           Object detection and recognition[Project]

    ·           Compressive Sensing Resources[Project]

    ·           Robust Real-Time Visual Tracking using Pixel-Wise Posteriors[Project]

    ·           Tracking-Learning-Detection[Project][OpenTLD/C++ Code]

    ·           the HandVu:vision-based hand gesture interface[Project]

    ·           Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities[Project]

     

    八、Kinect:

    ·           Kinect toolbox[Project]

    ·           OpenNI[Project]

    ·           zouxy09 CSDN Blog[Resource]

    ·           FingerTracker 手指跟踪[code]

     

    九、3D相关:

    ·           3D Reconstruction of a Moving Object[Paper] [Code]

    ·           Shape From Shading Using Linear Approximation[Code]

    ·           Combining Shape from Shading and Stereo Depth Maps[Project][Code]

    ·           Shape from Shading: A Survey[Paper][Code]

    ·           A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)[Project][Code]

    ·           Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code]

    ·           A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination[Paper][Code]

    ·           Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture[Project]

    ·           Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers[Code]

    ·           Learning 3-D Scene Structure from a Single Still Image[Project]

     

    十、机器学习算法:

    ·           Matlab class for computing Approximate Nearest Nieghbor (ANN) [Matlab class providing interface toANN library]

    ·           Random Sampling[code]

    ·           Probabilistic Latent Semantic Analysis (pLSA)[Code]

    ·           FASTANN and FASTCLUSTER for approximate k-means (AKM)[Project]

    ·           Fast Intersection / Additive Kernel SVMs[Project]

    ·           SVM[Code]

    ·           Ensemble learning[Project]

    ·           Deep Learning[Net]

    ·           Deep Learning Methods for Vision[Project]

    ·           Neural Network for Recognition of Handwritten Digits[Project]

    ·           Training a deep autoencoder or a classifier on MNIST digits[Project]

    ·          THE MNIST DATABASE of handwritten digits[Project]

    ·          Ersatz:deep neural networks in the cloud[Project]

    ·          Deep Learning [Project]

    ·          sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++[Project]

    ·          Weka 3: Data Mining Software in Java[Project]

    ·          Invited talk "A Tutorial on Deep Learning" by Dr. Kai Yu (余凯)[Video]

    ·          CNN - Convolutional neural network class[Matlab Tool]

    ·          Yann LeCun's Publications[Wedsite]

    ·          LeNet-5, convolutional neural networks[Project]

    ·          Training a deep autoencoder or a classifier on MNIST digits[Project]

    ·          Deep Learning 大牛Geoffrey E. Hinton's HomePage[Website]

    ·         Multiple Instance Logistic Discriminant-based Metric Learning (MildML) and Logistic Discriminant-based Metric Learning (LDML)[Code]

    ·         Sparse coding simulation software[Project]

    ·         Visual Recognition and Machine Learning Summer School[Software]

     

    十一、目标、行为识别Object, Action Recognition:

    ·           Action Recognition by Dense Trajectories[Project][Code]

    ·           Action Recognition Using a Distributed Representation of Pose and Appearance[Project]

    ·           Recognition Using Regions[Paper][Code]

    ·           2D Articulated Human Pose Estimation[Project]

    ·           Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[Paper][Code]

    ·           Estimating Human Pose from Occluded Images[Paper][Code]

    ·           Quasi-dense wide baseline matching[Project]

    ·           ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[Project]

    ·           Real Time Head Pose Estimation with Random Regression Forests[Project]

    ·           2D Action Recognition Serves 3D Human Pose Estimation[Project]

    ·           A Hough Transform-Based Voting Framework for Action Recognition[Project]

    ·           Motion Interchange Patterns for Action Recognition in Unconstrained Videos[Project]

    ·         2D articulated human pose estimation software[Project]

    ·         Learning and detecting shape models [code]

    ·         Progressive Search Space Reduction for Human Pose Estimation[Project]

    ·         Learning Non-Rigid 3D Shape from 2D Motion[Project]

     

    十二、图像处理:

    ·         Distance Transforms of Sampled Functions[Project]

    ·         The Computer Vision Homepage[Project]

    ·         Efficient appearance distances between windows[code]

    ·         Image Exploration algorithm[code]

    ·         Motion Magnification 运动放大 [Project]

    ·         Bilateral Filtering for Gray and Color Images 双边滤波器 [Project]

    ·         A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [Project]

                     

    十三、一些实用工具:

    ·           EGT: a Toolbox for Multiple View Geometry and Visual Servoing[Project] [Code]

    ·           a development kit of matlab mex functions for OpenCV library[Project]

    ·           Fast Artificial Neural Network Library[Project]

     

     

    十四、人手及指尖检测与识别:

    ·           finger-detection-and-gesture-recognition [Code]

    ·           Hand and Finger Detection using JavaCV[Project]

    ·           Hand and fingers detection[Code]



    十五、场景解释:

    ·           Nonparametric Scene Parsing via Label Transfer [Project]



    十六、光流Optical flow:

    ·         High accuracy optical flow using a theory for warping [Project]

    ·         Dense Trajectories Video Description [Project]

    ·         SIFT Flow: Dense Correspondence across Scenes and its Applications[Project]

    ·         KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker [Project]

    ·         Tracking Cars Using Optical Flow[Project]

    ·         Secrets of optical flow estimation and their principles[Project]

    ·         implmentation of the Black and Anandan dense optical flow method[Project]

    ·         Optical Flow Computation[Project]

    ·         Beyond Pixels: Exploring New Representations and Applications for Motion Analysis[Project]

    ·         A Database and Evaluation Methodology for Optical Flow[Project]

    ·         optical flow relative[Project]

    ·         Robust Optical Flow Estimation [Project]

    ·         optical flow[Project]


    十七、图像检索Image Retrieval

    ·           Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval [Paper][code]



    十八、马尔科夫随机场Markov Random Fields:

    ·         Markov Random Fields for Super-Resolution [Project]

    ·         A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors [Project]


    十九、运动检测Motion detection:

    ·         Moving Object Extraction, Using Models or Analysis of Regions [Project]

    ·         Background Subtraction: Experiments and Improvements for ViBe [Project]

    ·         A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications [Project]

    ·         changedetection.net: A new change detection benchmark dataset[Project]

    ·         ViBe - a powerful technique for background detection and subtraction in video sequences[Project]

    ·         Background Subtraction Program[Project]

    ·         Motion Detection Algorithms[Project]

    ·         Stuttgart Artificial Background Subtraction Dataset[Project]

    ·         Object Detection, Motion Estimation, and Tracking[Project]

    
    展开全文
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