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ReMP: Rectified Metric Propagation for Few-Shot Learning

ReMP: Rectified Metric Propagation for Few-Shot Learning

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Yang Li, Chunyuan Yu, Ping Chen, Changyou Univ Buffalo Buffalo NY 14260 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665448994

Few-shot learning features the capability of generalizing from a few examples. In this paper, we first identify that a discriminative feature space, namely a rectified metric space, that is learned to maintain the metric consistency from training to testing, is an essential component to the success of metric-based few-shot learning. Numerous analyses indicate that a simple modification of the objective can yield substantial performance gains. The resulting approach, called rectified metric propagation (ReMP), further optimizes an attentive prototype propagation network, and applies a repulsive force to make confident predictions. Extensive experiments demonstrate that the proposed ReMP is effective and efficient, and outperforms the state of the arts on various standard few-shot learning datasets.

关键词： Training computer vision conferences Force Prototypes Performance gain Extraterrestrial measurements

来源：评论

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Three Gaps for Quantisation in Learned Image Compression

Three Gaps for Quantisation in Learned Image Compression

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Pan, Shi Finlay, Chris Besenbruch, Chri Knottenbelt, William Imperial Coll London Dept Comp London England DeepRender London England

ISBN: (纸本)9781665448994

Learned lossy image compression has demonstrated impressive progress via end-to-end neural network training. However, this end-to-end training belies the fact that lossy compression is inherently not differentiable, due to the necessity of quantisation. To overcome this difficulty in training, researchers have used various approximations to the quantisation step. However, little work has studied the mechanism of quantisation approximation itself. We address this issue, identifying three gaps arising in the quantisation approximation problem. These gaps are visualised, and show the effect of applying different quantisation approximation methods. Following this analysis, we propose a Soft-STE quantisation approximation method, which closes these gaps and demonstrates better performance than other quantisation approaches on the Kodak dataset.

关键词： Training Visualization computer vision Quantization (signal) Image coding conferences Neural networks

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Sample-free white-box out-of-distribution detection for deep learning

Sample-free white-box out-of-distribution detection for deep...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Begon, Jean-Michel Geurts, Pierre Univ Liege Liege Belgium

ISBN: (纸本)9781665448994

Being able to detect irrelevant test examples with respect to deployed deep learning models is paramount to properly and safely using them. In this paper, we address the problem of rejecting such out-of-distribution (OOD) samples in a fully sample-free way, i.e., without requiring any access to in-distribution or OOD samples. We propose several indicators which can be computed alongside the prediction with little additional cost, assuming white-box access to the network. These indicators prove useful, stable and complementary for OOD detection on frequently-used architectures. We also introduce a surprisingly simple, yet effective summary OOD indicator. This indicator is shown to perform well across several networks and datasets and can furthermore be easily tuned as soon as samples become available. Lastly, we discuss how to exploit this summary in real-world settings.

关键词： Deep learning computer vision Filtering Computational modeling conferences computer architecture Data models

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CE-PeopleSeg: Real-time people segmentation with 10% CPU usage for video conference

CE-PeopleSeg: Real-time people segmentation with 10% CPU usa...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Jiang, Ziyu He, Zhenhua Huang, Xueqin Yang, Zibin Tan, Pearl Texas A&M Univ College Stn TX 77843 USA EEO Washington DC USA

ISBN: (纸本)9781665448994

Nowadays, video conference solutions are widely adopted for companies, education, and government. People segmentation is crucial for supporting virtual background, an essential video conference function to protect users' privacy. This paper demonstrated a people segmentation framework called CE-PeopleSeg, which employed an efficient segmentation method, structural pruning, and dynamic frame skipping techniques, leading to a fast inference speed on CPU. Our extensive experiments show that the proposed CE-PeopleSeg can achieve a high prediction mIoU of 87.9% on Supervised People Dataset while reaching a real-time inference speed of 32.40 fps on CPU with very low usage of 10%. Our code would be released at https://***/geekJZY/***.

关键词： Visualization Privacy Government Education Graphics processing units Switches Streaming media

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Color Me Good: Branding in the Coloring Style of Movie Posters

Color Me Good: Branding in the Coloring Style of Movie Poste...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Agrawal, Rishabh Sivaprasad, Sarath Pedanekar, Niranjan TCS Res Tata Consultancy Serv Pune 411013 Maharashtra India

ISBN: (纸本)9781665448994

Brand logos are often rendered in a different style based on a context such as an event promotion. For example, Warner Bros. uses a different variety of their brand logo for different movies for promotion and aesthetic appeal. In this paper, we propose an automated method to render brand logos in the coloring style of branding material such as movie posters. For this, we adopt a photo-realistic neural style transfer method using movie posters as the style source. We propose a color-based image segmentation and matching method to assign style segments to logo segments. Using these, we render the well-known Warner Bros. logo in the coloring style of 141 movie posters. We also present survey results where 287 participants rate the machine-stylized logos for their representativeness and visual appeal.

关键词： Image segmentation Visualization computer vision Image color analysis conferences Semantics Brightness

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One-Shot GAN: Learning to Generate Samples from Single Images and Videos

One-Shot GAN: Learning to Generate Samples from Single Image...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sushko, Vadim Gall, Juergen Khoreva, Anna Bosch Ctr Artificial Intelligence Stuttgart Germany Univ Bonn Bonn Germany

ISBN: (纸本)9781665448994

Training GANs in low-data regimes remains a challenge, as overfitting often leads to memorization or training divergence. In this work, we introduce One-Shot GAN that can learn to generate samples from a training set as little as one image or one video. We propose a two-branch discriminator, with content and layout branches designed to judge the internal content separately from the scene layout realism. This allows synthesis of visually plausible, novel compositions of a scene, with varying content and layout, while preserving the context of the original sample. Compared to previous single-image GAN models, One-Shot GAN achieves higher diversity and quality of synthesis. It is also not restricted to the single image setting, successfully learning in the introduced setting of a single video.

关键词： Training Visualization computer vision conferences Computational modeling Layout Tools

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Reconsidering CO2 emissions from computer vision

Reconsidering CO2 emissions from Computer Vision

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Fu, Andre Hosseini, Mahdi S. Plataniotis, Konstantinos N. Univ Toronto Toronto ON Canada Univ New Brunswick Fredericton NB Canada

ISBN: (纸本)9781665448994

Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including those in the computer vision (CV) community. In this work, we analyze the total cost of CO2 emissions by breaking it into (1) the architecture creation cost and (2) the life-time evaluation cost. We show that over time, these costs are non-negligible and are having a direct impact on our future. Importantly, we conduct an ethical analysis of how the CV-community is unintentionally overlooking its own ethical AI principles by emitting this level of CO2. To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.

关键词： computer vision Climate change conferences computer architecture Pressing pattern recognition Artificial intelligence

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Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing

Dressing in Order: Recurrent Person Image Generation for Pos...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cui, Aiyu McKee, Daniel Lazebnik, Svetlana Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665448994

We propose a flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks. The key to DiOr is a novel recurrent generation pipeline to sequentially put garments on a person, so that trying on the same garments in different orders will result in different looks. Our system can produce dressing effects not achievable by existing work, including different interactions of garments (e.g., wearing a top tucked into the bottom or over it), as well as layering of multiple garments of the same type (e.g., jacket over shirt over t-shirt). DiOr explicitly encodes the shape and texture of each garment, enabling these elements to be edited separately. Extensive evaluations show that DiOr outperforms other recent methods like ADGAN [18] in terms of output quality, and handles a wide range of editing functions for which there is no direct supervision.

关键词： computer vision Shape Image synthesis conferences Clothing Pipelines pattern recognition

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Shot in the Dark: Few-Shot Learning with No Base-Class Labels

Shot in the Dark: Few-Shot Learning with No Base-Class Label...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Zitian Maji, Subhransu Learned-Miller, Erik Univ Massachusetts Amherst Amherst MA 01003 USA

ISBN: (纸本)9781665448994

Few-shot learning aims to build classifiers for new classes from a small number of labeled examples and is commonly facilitated by access to examples from a distinct set of 'base classes'. The difference in data distribution between the test set (novel classes) and the base classes used to learn an inductive bias often results in poor generalization on the novel classes. To alleviate problems caused by the distribution shift, previous research has explored the use of unlabeled examples from the novel classes, in addition to labeled examples of the base classes, which is known as the transductive setting. In this work, we show that, surprisingly, off-the-shelf self-supervised learning outperforms transductive few-shot methods by 3.9% for 5-shot accuracy on miniImageNet without using any base class labels. This motivates us to examine more carefully the role of features learned through self-supervision in few-shot learning. Comprehensive experiments are conducted to compare the transferability, robustness, efficiency, and the complementarity of supervised and self-supervised features.

关键词： computer vision conferences Supervised learning Robustness pattern recognition

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Perceptual Loss for Robust Unsupervised Homography Estimation

Perceptual Loss for Robust Unsupervised Homography Estimatio...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Koguciuk, Daniel Arani, Elahe Zonooz, Bahram NavInfo Europe Adv Res Lab Eindhoven Netherlands

ISBN: (纸本)9781665448994

Homography estimation is often an indispensable step in many computer vision tasks. The existing approaches, however, are not robust to illumination and/or larger viewpoint changes. In this paper, we propose bidirectional implicit Homography Estimation (biHomE) loss for unsupervised homography estimation. biHomE minimizes the distance in the feature space between the warped image from the source viewpoint and the corresponding image from the target viewpoint. Since we use a fixed pre-trained feature extractor and the only learnable component of our framework is the homography network, we effectively decouple the homography estimation from representation learning. We use an additional photometric distortion step in the synthetic COCO dataset generation to better represent the illumination variation of the real-world scenarios. We show that biHomE achieves state-of-the-art performance on synthetic COCO dataset, which is also comparable or better compared to supervised approaches. Furthermore, the empirical results demonstrate the robustness of our approach to illumination variation compared to existing methods.

关键词： Degradation computer vision conferences Estimation Lighting Feature extraction Distortion

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