• Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is ...
  • A Comparison of Affine Region Detectors.pdf K. MIKOLAJCZYK University of Oxford论文
  • Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by pubhshing worldwide in Oxford ...
  • this one is reasonable, since it is under the consideration of the engineering science


    this one is reasonable, since it is under the consideration of the engineering science and my supervisor is in computer science department.

  • Books on big data tend to fall into one of two categories: either they offer no explanation as to how things actually work or they are highly mathematical textbooks suitable only for graduate students...
  • <div><p>Style designed to fit the Cite Them Right Online version of Harvard in use at Oxford Brookes University. Changes to Anon citations compared to other versions of Harvard.</p><p>该提问来源于开源...
  • 'The strength of the book is that examples from classroom research are used to demonstrate how meta-language about grammar and meaning can be included, rather than added separately, in a content ...
  • Three of the University's scientists have been elected to the prestigious Fellowship of the Academy of Medical Sciences for their contribution to biomedical and health research, the generation of new

    Three of the University's scientists have been elected to the prestigious Fellowship of the Academy of Medical Sciences for their contribution to biomedical and health research, the generation of new knowledge in medical sciences, and its translation into benefits to society.

    They are Professor James Berkley, Professor Susan Lea, and Professor Philip K. Maini FRS.

    This year's elected Fellows, 46 in total, have expertise that spans women’s health, immunology, public health and infectious disease among many other fields.

    Professor James Berkley is based at the KEMRI Wellcome Trust Research Programme, Kenya, and is Professor of Paediatric Infectious Diseases at the Centre for Tropical Medicine and Global Health; Professor Susan Lea is Director of the Central Oxford Structural Microscopy and Imaging Centre & Professor of Microbiology at the Sir William Dunn School of Pathology; Professor Philip K. Maini FRS is Professor of Mathematical Biology and Director of the Wolfson Centre for Mathematical Biology.

    Professor Sir Robert Lechler PMedSci, President of the Academy of Medical Sciences said: ‘As we elect new Fellows each year, the Academy continues to grow as a unique and productive hub of the best minds in UK medical science. Our expanding and evolving Fellowship allows the Academy to play a vital role in tackling the major health challenges facing society today and in the future.

    ‘The election of these 46 exceptional biomedical and health researchers is a well-deserved honour. Each newly elected Fellow has made an outstanding impact in the community, contributing to the development of better healthcare - from fundamental scientific understanding to the prevention, diagnosis and treatment of disease. I am delighted to welcome them to the Academy and greatly look forward to working with them all in the future.’

    The new Fellows will be formally admitted to the Academy at a ceremony on 28 June 2017.

  • Sara Tulloch, «The Oxford Dictionary of New Words: A Popular Guide to Words in the News»Oxford University Press, USA | ISBN 0192830775 | (April 1, 1993) | PDF | 1.15 Mb | 336 pagesDOWNLOAD:http:

    Sara Tulloch, «The Oxford Dictionary of New Words: A Popular Guide to Words in the News»
    Oxford University Press, USA | ISBN 0192830775 | (April 1, 1993) | PDF | 1.15 Mb | 336 pages



    What does "mirror-shades group" mean? (Is it a solar power array, a school of science fiction, a slang word for "corporate raiders"?) Who first coined the word "factoid"? When and where does the "triple witching hour" occur? On what kid's show was the cry "Cowabunga" first heard?

    In The Oxford Dictionary of New Words, Sara Tulloch examines over two thousand new words and phrases that have become part of our daily lives in the last decade, in 750 articles that provide pronunciation, definition, etymology, informal history, and sample sentences. Drawing words from politics, the environmental movement, computers and technology, business, sports, entertainment, and many other areas, Tulloch goes beyond the usual informative but narrow dictionary entry to offer readers a rich history of the recent changes not only in our language but in our culture as well. Just
    skimming the headwords is like fast-forwarding through the eighties: bailout, cocooning, deniability, the disappeared, glasnost, lambada, safe sex, spin doctor, fun run, insider trading, genetic fingerprinting, thirtysomething, designer water, liposuction, Cablevision, gentrification, intifada, and DINK (Double Income, No Kids). And the histories that Tulloch provides are so interesting that even if you know the meaning of a word you will find the article fascinating.

    Here then is a resource that is both useful and intriguing, the first place to turn when faced with such new words and phrases as "acyclovir," "magnetic resonance imaging," "Alar" or "computer footprint," as well as a browser's delight, a goldmine of language for word lovers

    From Library Journal
    In this new dictionary, about 2000 contemporary words and phrases are given international Phonetic Alphabet pronunciation, one or more definitions, accounts of origin and usage, and readable, useful summaries of their background. All the information provided is first-rate. Examples of usage are quoted, with citations, and a set of 11 "subject icons" highlight graphically the fields of interest (business, drugs, music, etc.) in which the terms operate. The entries go beyond the usual basics found in most dictionaries and, in detail and scope, comprise a sort of mirror of word usage in recent years. Purists might see some of the terms as barbarisms best ignored, e.g., glocal and ungreen . Though British viewpoint and spelling are employed, the rich tradition of excellence in dictionary making at Oxford is well served with this outstanding volume.

    3rd Edition.

  • The Oxford Dictionary of English (ODE) is a single-volume English dictionary published by Oxford University Press, first published in 1998 as The New Oxford Dictionary of English (NODE). The word "new...
  • Oxford Modern English Grammar ,Department of English Language and Literature University College London
  • Oxford NLP lecture

    2017-11-17 15:23:37
    This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an advanced course ...
  • 标题:用于全景分割的全卷积网络 ...机构:Chinese University of Hong Kong University of Oxford University of Hong Kong MEGVII Technology 论文地址:https://arxiv.org/pdf/2012.00720.pdf 项目地址:https://

    作者:Yanwei Li, Hengshuang Zhao, Xiaojuan Qi, Liwei Wang, Zeming Li, Jian Sun, Jiaya Jia
    机构:Chinese University of Hong Kong University of Oxford University of Hong Kong MEGVII Technology


    在本文中,我们提出了一种概念上简单,强大且有效的全景图分割框架,称为Panoptic FCN。我们的方法旨在在统一的全卷积网络中表示和预测前景事物(thing)和背景事物(stuff)。Panoptic FCN使用提出的内核生成器(kernel generator)将每个对象实例或填充(stuff )类别编码为特定的内核权重(kernel weight),并通过直接将高分辨率特征卷积来产生预测。使用这种方法,可以在一个简单的生成内核然后分段工作流中分别满足事物和事物的实例感知和语义上一致的属性。在没有额外的框用于定位或实例分离的情况下,所提出的方法在单尺度输入的COCO,Cityscapes和Mapillary Vistas数据集上的性能优于以前的box-based and box-free的方式。

    疑问:所谓的kernel generator指的是卷积核的kernel weight的生成吗?感觉有点像deformable conv的操作。


    全景分割的一个主要难点在于:统一表示来自thing和stuff要求的冲突属性(conflicting properties)。实例对象通过instance-aware features区分(比如solo中的位置大小),而stuff则更加偏好于semantically consistent characters(连续性特征)。因此往往两者的模型是分开设计的。因此,需要统一的表示形式来弥合这一差距。
    本篇文章提出了Panoptic FCN,一个全卷积框架。

    Panoptic FCN的关键思想是在完全卷积的网络结构中用生成的内核统一地表示和预测thing和stuff

    具体设计是包括两个方面:内核生成器(kernel generator)和特征编码器(feature encoder),分别用于内核权重生成和共享特征编码。具体来说,在内核生成器中,我们参考基于点的目标检测器方式,并利用position head分别通过对象中心(object centers)和填充区域(stuff regions)对前景对象(objects)和背景填充物(stuff)进行定位和分类 。 然后,我们从内核头中选择具有相同位置的内核权重来表示相应的实例。 对于上述实例感知和语义一致性,还提出了一个称为内核融合kernel fusion的内核级操作,该操作合并了预测具有相同身份或语义类别的内核权重。 借助特征编码器,该编码器保留了具有细节的高分辨率特征,可以通过直接对生成的内核进行卷积来生成事物的预测。

    To this end, kernel generator and feature encoder are
    respectively designed for kernel weights generation and shared feature encoding. Specifically, in kernel genera- tor, we draw inspirations from point-based object detec- tors [19, 53] and utilize the position head to locate as well as classify foreground objects and background stuff by ob- ject centers and stuff regions, respectively. Then, we se- lect kernel weights with the same positions from the kernel head to represent corresponding instances. For the instance- awareness and semantic-consistency described above, a kernel-level operation, called kernel fusion, is further pro- posed, which merges kernel weights that are predicted to have the same identity or semantic category. With a naive feature encoder, which preserves the high-resolution feature with details, each prediction of things and stuff can be produced by convolving with generated kernels directly.

    Panoptic FCN在没有更多花销的情况下,在效率上优于以前的方法,并且在COCO val和test dev数据集上分别达到44.3%PQ和47.5%PQ。 同时,它在很大程度上超越了所有类似的无盒方法,并在Cityscapes和Mapillary Vistas val 数据集分别为61.4%PQ和36.9%PQ

    疑问:看到这里,应该是用kernel generator来生成对于不同thing和stuff的不同kernel weight,然后利用特征编码器的结果和生成的kernel weight卷积,以生成最后的结果? 下面关注kernel generator是怎么操作的


    • Panoptic Segmentation
    • Instance Segmentation
      Box-based:Mask R-CNN , PANet , YOLACT , CenterMask , and PointINS.
      Box-free:SGN, SSAP , TensorMask, and SOLO.
    • Semantic Segmentation

    Panoptic FCN

    Panoptic FCN在概念上很简单:

    • 引入了内核生成器来为具有不同类别的thing和stuff生成内核权重
    • 内核融合旨在将来自多个阶段的具有相同标识的内核权重进行合并
    • 特征编码器用于对高分辨率特征进行编码

    Kernel Generator

    Kernel Generator的输入是FPN 中第i阶段的单阶段特征XiX_i,输出是对于thing LithL^{th}_i和stuff ListL^{st}_i的带有位置信息的Kernel weight map GiG_i 。kernel generator模块包含两个部分,如上面的图,其中的position head是用来对定位和分类的,kernel head则是kernel权重生成。

    1、Position head

    输入是FPN 中第i阶段的单阶段特征XiX_i,经过堆叠的卷积块进一步编码特征得到XiX'_i

    然后根据对象中心(object center)和填充区域(stuff regions)来分别表示每个个体和填充类别的位置。(同一类别的stuff看作一个实例)这样,整个图都可以用区分实例的方法来进行thing和stuff的位置和分类了。

    输出:object map LithRNth×Wi×HiL^{th}_i ∈ R^{N_{th}×W_i×H_i} and stuff mapListRNst×Wi×HiL^{st}_i ∈ R^{N_{st}×W_i×H_i}

    为了优化这个输出,文章对于ground truth的生成做了处理:
    对于类别c中的第k个对象,将正关键点(positive key-points这里的正关键点指的是???)利用高斯核划分到热图(heatmap)Yith[01]Nth×Wi×HiY^{th}_i∈[0,1]^{N_{th}×W_i×H_i}的第c个通道上。(也就是说用通道表示类别,不同关键点表示不同的对象)
    这里的xkandykx^-_k and y^-_k表示第k个对象的center。其中ok2o^2_k 是表示对象尺寸的标准偏差。

    oko_k = xk/n[xk/n],yk/n[yk/n](x_k/n-[x_k/n],y_k/n-[y_k/n])

    解释1: 在下采样阶段中,(x,y)位置的像素被映射为([x/n],[y/n][x/n],[y/n]),而对其还原时,会存在一定精度上的损失,对IOU造成影响,因此,在对其remap到输入尺寸之前,预测位置偏移对角点的位置进行微调。
    解释2:因为feature map比原始图像肯定是小的,所以在由feature map映射到原图像位置的时候就存在一定偏移,比如原始图像的位置为(220,220),下采样的比率为3,则对应的feature map的位置就是(73,73),而要映射回去的时候却出现了问题,因为73*3 = 219,所以是有偏移的,这个偏移可以通过网络学习,这样不仅可以消去偏移,而且可以让临近点尽可能忘gt靠拢。


    而对于stuff regions,通过对one-hot semantic annotation双线性插值到相应大小(???)得到:Yist[0,1]Nst×Wi×HiY^{st}_i ∈ [0, 1]N^{st}×W_i×H_i

    独热编码即 One-Hot 编码,又称一位有效编码,其方法是使用N位状态寄存器来对N个状态进行编码,每个状态都由他独立的寄存器位,并且在任意时候,其中只有一位有效。参考:https://blog.csdn.net/linh47/article/details/79000574?utm_medium=distribute.pc_relevant.none-task-blog-baidujs_title-11&spm=1001.2101.3001.4242

    所以position head的损失优化函数:

    2、Kernel head


    然后堆叠的卷积块(如上面的框架流程图所示),从XiX''_i生成核权重图(kernel weight map)GiR(Ce)×Wi×HiG_i∈R^{(C_e)}×W_i×H_i

    有了从position head预测的DithD^{th}_iDistD^{st}_i,选择在GiG_i中具有相同坐标的内核权重来表示相应的实例。例如,假设候选xcycDith(x_c,y_c)∈D^{th}_i,选择内核权重Gi,:,xc,ycRCe×1×1G_i,:,x_c,y_c∈R^{C_e×1×1}以生成具有预测类别c的结果。


    3、Kernel Fusion
    详细点讲就是:从所有FPN stage得到的kernel weight DthD^{th}DstD^{st},第j个kernel weight KjRCe×1×1=AvgPool(Gj)K_j ∈ R^{C_e×1×1} = AvgPool(G'_j)

    the candidate set GjG'_j= {Gm:ID(Gm)=ID(Gj)G_m : ID(G_m) = ID(G_j)} includes all the
    kernel weights, which are predicted to have the same identity ID with


    利用所提出的方法,可以将Kth=K1th...KmthRM×Ce×1×1K^{th} = {K^{th}_1,...,K^{th}_m}∈R^{M×C_e×1×1}的每个核权重KjthK^{th}_j视为单个对象的编码,其中对象总数为M 。因此,具有相同标识的内核将合并为单个编码的事物,并且KthK^{th}中的每个内核都代表一个单独的对象,这满足了事物的实例感知能力。 同时,Kst=K1st...KnstRN×Ce×1×1K^{st} = {K^{st}_1,...,K^{st}_n}∈R^{N×C_e×1×1}的核权重KjstK^{st}_j表示对所有第j类像素的编码,其中现有填充数为N。 这种方法将具有相同语义类别的内核融合到单个嵌入中,从而保证了东西的语义一致性。 因此,thing和stuff所要求的属性都可以通过提出的内核融合操作来实现

    Feature Encoder

    输入:从FPN P2 或者所有Stage融合的高分辨率特征 FhRCe×W/4×H/4F_h ∈ R^{C_e×W/4×H/4}

    后续处理和上面的Kernel Generator的Kernel Head类似,加入position信息,用堆叠卷积块进一步采样

    输出: FeRCe×W/4×H/4F_e ∈ R^{C_e×W/4×H/4}


    Pj=KjFjeP_j = K_j ⊗F^e_j

    where Pj denotes the j-th prediction, and ⊗ indicates the convolutional operation. That meansM+N kernel weights generateM+N instance predictions with resolutionW/4× H/4 for the whole image. Consequently,


    Consequently, the panoptic result can be produced with a simple
    process [17]


    Training and Inference

    训练采用了修改后的Dice Loss:




    主要实验数据集:COCO、Cityscopes、Mapillary Vistas


    3、对于kernel fusion部分的thres门限值设置,在0.9左右结果差别不大
    5、对于特征编码器的输入,feature的生成方式,FPN-P2、FPN-Summed、Semantic FPN中Semantic FPN最好
    6、另外对于dice loss k的设置,3以后没有大变换,7最好
    8、最后还有一个上限边界分析:采用ground truth的 position和 class,最后的PQ提升22.3个点。


    虽然现在COCO上已经刷到PQ 50多,Cityscopes PQ 70了…


    这篇文章最值得思考的地方应该就是thing和stuff 的一致性表示:将stuff也看成instance。

  • world university rank top 20

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