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Discriminator-Cooperated Feature Map Distillation for GAN Compression

Discriminator-Cooperated Feature Map Distillation for GAN Co...

引用

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

作者： Hu, Tie Lin, Mingbao You, Lizhou Chao, Fei Ji, Rongrong Xiamen Univ Sch Informat MAC Lab Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Xiamen Peoples R China Tencent Youtu Lab Shanghai Peoples R China Xiamen Univ Shenzhen Res Inst Xiamen Peoples R China

ISBN: (纸本)9798350301298

Despite excellent performance in image generation, Generative Adversarial Networks (GANs) are notorious for its requirements of enormous storage and intensive computation. As an awesome "performance maker", knowledge distillation is demonstrated to be particularly efficacious in exploring low-priced GANs. In this paper, we investigate the irreplaceability of teacher discriminator and present an inventive discriminator-cooperated distillation, abbreviated as DCD, towards refining better feature maps from the generator. In contrast to conventional pixel-to-pixel match methods in feature map distillation, our DCD utilizes teacher discriminator as a transformation to drive intermediate results of the student generator to be perceptually close to corresponding outputs of the teacher generator. Furthermore, in order to mitigate mode collapse in GAN compression, we construct a collaborative adversarial training paradigm where the teacher discriminator is from scratch established to co-train with student generator in company with our DCD. Our DCD shows superior results compared with existing GAN compression methods. For instance, after reducing over 40x MACs and 80x parameters of CycleGAN, we well decrease FID metric from 61.53 to 48.24 while the current SoTA method merely has 51.92. This work's source code has been made accessible at https://***/poopit/DCD-official.

关键词： Efficient and scalable vision

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Temporally-Consistent Video Semantic Segmentation with Bidirectional Occlusion-guided Feature Propagation

Temporally-Consistent Video Semantic Segmentation with Bidir...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Baghbaderani, Razieh Kaviani Li, Yuanxin Wang, Shuangquan Qi, Hairong Samsung Semicond Inc SOC R&D San Jose CA 95134 USA Univ Tennessee Knoxville TN USA

ISBN: (纸本)9798350318920;9798350318937

Despite recent progress in static image segmentation, video segmentation is still challenging due to the need for an accurate, fast, and temporally consistent model. Conducting per-frame static image segmentation on a video is not acceptable since it is computationally prohibitive and prone to temporal inconsistency. In this paper, we present bidirectional occlusion-guided feature propagation (BOFP) method with the goal of improving temporal consistency of segmentation results without sacrificing segmentation accuracy, while at the same time keeping the operations at a low computation cost. It leverages temporal coherence in the video by feature propagation from keyframes to other frames along the motion paths in both forward and backward directions. We propose an occlusion-based attention network to estimate the distorted areas based on bidirectional optical flows, and utilize them as cues for correcting and fusing the propagated features. Extensive experiments on benchmark datasets demonstrate that the proposed BOFP method achieves superior performance in terms of temporal consistency while maintaining comparable level of segmentation accuracy at a low computation cost, striking a great balance among the three performance metrics essential to evaluate video segmentation solutions.

关键词： Algorithms Algorithms Image recognition and understanding Video recognition and understanding

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Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection

Explicit Boundary Guided Semi-Push-Pull Contrastive Learning...

引用

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

作者： Yao, Xincheng Li, Ruoqi Zhang, Jing Sun, Jun Zhang, Chongyang Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Acad Mil Sci Res Inst Syst Engn Beijing Peoples R China

ISBN: (纸本)9798350301298

Most anomaly detection (AD) models are learned using only normal samples in an unsupervised way, which may result in ambiguous decision boundary and insufficient discriminability. In fact, a few anomaly samples are often available in real-world applications, the valuable knowledge of known anomalies should also be effectively exploited. However, utilizing a few known anomalies during training may cause another issue that the model may be biased by those known anomalies and fail to generalize to unseen anomalies. In this paper, we tackle supervised anomaly detection, i.e., we learn AD models using a few available anomalies with the objective to detect both the seen and unseen anomalies. We propose a novel explicit boundary guided semi-push-pull contrastive learning mechanism, which can enhance model's discriminability while mitigating the bias issue. Our approach is based on two core designs: First, we find an explicit and compact separating boundary as the guidance for further feature learning. As the boundary only relies on the normal feature distribution, the bias problem caused by a few known anomalies can be alleviated. Second, a boundary guided semi-push-pull loss is developed to only pull the normal features together while pushing the abnormal features apart from the separating boundary beyond a certain margin region. In this way, our model can form a more explicit and discriminative decision boundary to distinguish known and also unseen anomalies from normal samples more effectively. Code will be available at https://***/xcyao00/BGAD.

关键词： detection recognition: Categorization retrieval

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Label-Free Synthetic Pretraining of Object Detectors

Label-Free Synthetic Pretraining of Object Detectors

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Law, Hei Deng, Jia Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350318920;9798350318937

We propose a new approach, Synthetic Optimized Layout with Instance Detection (SOLID), to pretrain object detectors with synthetic images. Our "SOLID" approach consists of two main components: (1) generating synthetic images using a collection of unlabelled 3D models with optimized scene arrangement;(2) pretraining an object detector on "instance detection" task-given a query image depicting an object, detecting all instances of the exact same object in a target image. Our approach does not need any semantic labels for pretraining and allows the use of arbitrary, diverse 3D models. Experiments on COCO show that with optimized data generation and a proper pretraining task, synthetic data can be highly effective data for pretraining object detectors. In particular, pretraining on rendered images achieves performance competitive with pretraining on real images while using significantly less computing resources. Code is available at https://***/princeton-vl/SOLID.

关键词： Algorithms Image recognition and understanding

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Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective

Solving Oscillation Problem in Post-Training Quantization Th...

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

作者： Ma, Yuexiao Li, Huixia Zheng, Xiawu Xiao, Xuefeng Wang, Rui Wen, Shilei Pan, Xin Chao, Fei Ji, Rongrong Xiamen Univ Minist Educ China Key Lab Multimedia Trusted Percept & Efficient Co Sch Informat Xiamen 361005 Peoples R China ByteDance Inc Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Xiamen Univ Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Post-training quantization (PTQ) is widely regarded as one of the most efficient compression methods practically, benefitting from its data privacy and low computation costs. We argue that an overlooked problem of oscillation is in the PTQ methods. In this paper, we take the initiative to explore and present a theoretical proof to explain why such a problem is essential in PTQ. And then, we try to solve this problem by introducing a principled and generalized framework theoretically. In particular, we first formulate the oscillation in PTQ and prove the problem is caused by the difference in module capacity. To this end, we define the module capacity (ModCap) under data-dependent and data-free scenarios, where the differentials between adjacent modules are used to measure the degree of oscillation. The problem is then solved by selecting top-k differentials, in which the corresponding modules are jointly optimized and quantized. Extensive experiments demonstrate that our method successfully reduces the performance drop and is generalized to different neural networks and PTQ methods. For example, with 2/4 bit ResNet-50 quantization, our method surpasses the previous state-of-the-art method by 1.9%. It becomes more significant on small model quantization, e.g. surpasses BRECQ method by 6.61% on MobileNetV2 x0.5.

关键词： Efficient and scalable vision

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MIVC: Multiple Instance Visual Component for Visual-Language Models

MIVC: Multiple Instance Visual Component for Visual-Language...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Wu, Wenyi Li, Qi Zhong, Wenliang Huang, Junzhou Amazon 424 5th Ave New York NY 10018 USA Amazon 410 Terry Ave N Seattle WA USA Univ Texas Arlington 500 UTA Blvd Arlington TX USA

ISBN: (纸本)9798350318920;9798350318937

vision-language models have been widely explored across a wide range of tasks and achieve satisfactory performance. However, it's under-explored how to consolidate entity understanding through a varying number of images and to align it with the pre-trained language models for generative tasks. In this paper, we propose MIVC, a general multiple instance visual component to bridge the gap between various image inputs with off-the-shelf vision-language models by aggregating visual representations in a permutation-invariant fashion through a neural network. We show that MIVC could be plugged into the visual-language models to improve the model performance consistently on visual question answering, classification and captioning tasks on a public available e-commerce dataset with multiple images per product. Furthermore, we show that the component provides insight into the contribution of each image to the downstream tasks.

关键词： Algorithms Applications Commercial / retail Image recognition and understanding

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NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud

NerVE: Neural Volumetric Edges for Parametric Curve Extracti...

引用

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

作者： Zhu, Xiangyu Du, Dong Chen, Weikai Zhao, Zhiyou Nie, Yinyu Han, Xiaoguang CUHKSZ SSE Shenzhen Peoples R China CUHKSZ FNii Shenzhen Peoples R China Tencent Amer Palo Alto CA USA Tech Univ Munich Munich Germany

ISBN: (纸本)9798350301298

Extracting parametric edge curves from point clouds is a fundamental problem in 3D vision and geometry processing. Existing approaches mainly rely on keypoint detection, a challenging procedure that tends to generate noisy output, making the subsequent edge extraction error-prone. To address this issue, we propose to directly detect structured edges to circumvent the limitations of the previous point-wise methods. We achieve this goal by presenting NerVE, a novel neural volumetric edge representation that can be easily learned through a volumetric learning framework. NerVE can be seamlessly converted to a versatile piece-wise linear (PWL) curve representation, enabling a unified strategy for learning all types of free-form curves. Furthermore, as NerVE encodes rich structural information, we show that edge extraction based on NerVE can be reduced to a simple graph search problem. After converting NerVE to the PWL representation, parametric curves can be obtained via off-the-shelf spline fitting algorithms. We evaluate our method on the challenging ABC dataset [19]. We show that a simple network based on NerVE can already outperform the previous state-of-the-art methods by a great margin.

关键词： vision + graphics

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recognition of Unseen Bird Species by Learning from Field Guides

Recognition of Unseen Bird Species by Learning from Field Gu...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Rodriguez, Andres C. D'Aronco, Stefano Daudt, Rodrigo Caye Wegner, D. Schindler, Konrad Swiss Fed Inst Technol EcoVis Lab Photogrammetry & Remote Sensing Zurich Switzerland Univ Zurich Inst Computat Sci Zurich Switzerland

ISBN: (纸本)9798350318920;9798350318937

We exploit field guides to learn bird species recognition, in particular zero-shot recognition of unseen species. Illustrations contained in field guides deliberately focus on discriminative properties of each species, and can serve as side information to transfer knowledge from seen to unseen bird species. We study two approaches: (1) a contrastive encoding of illustrations, which can be fed into standard zero-shot learning schemes;and (2) a novel method that leverages the fact that illustrations are also images and as such structurally more similar to photographs than other kinds of side information. Our results show that illustrations from field guides, which are readily available for a wide range of species, are indeed a competitive source of side information for zero-shot learning. On a subset of the iNaturalist2021 dataset with 749 seen and 739 unseen species, we obtain a classification accuracy of unseen bird species of 12% @top-1 and 38% @top-10, which shows the potential of field guides for challenging real-world scenarios with many species. Our code is available at https: //***/ac-rodriguez/zsl_billow.

关键词： Algorithms and algorithms Animals / Insects Applications formulations Machine learning architectures

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Alpha-wolves and Alpha-mammals: Exploring Dictionary Attacks on Iris recognition Systems

Alpha-wolves and Alpha-mammals: Exploring Dictionary Attacks...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Banerjee, Sudipta Jain, Anubhav Jiang, Zehua Memon, Nasir Togelius, Julian Ross, Arun NYU New York NY 10003 USA Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9798350370287;9798350370713

A dictionary attack in a biometric system entails the use of a small number of strategically generated images or templates to successfully match with a large number of identities, thereby compromising security. We focus on dictionary attacks at the template level, specifically the IrisCodes used in iris recognition systems. We present an hitherto unknown vulnerability wherein we mix IrisCodes using simple bitwise operators to generate alpha-mixtures -alpha-wolves (combining a set of "wolf" samples) and alpha-mammals (combining a set of users selected via search optimization) that increase false matches. We evaluate this vulnerability using the IITD, CASIA-IrisV4-Thousand and Synthetic datasets, and observe that an alpha-wolf (from two wolves) can match upto 71 identities @FMR=0.001%, while an alpha-mammal (from two identities) can match upto 133 other identities @FMR=0.01% on the IITD dataset.

关键词： Mammals

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Do VSR Models Generalize Beyond LRS3?

Do VSR Models Generalize Beyond LRS3?

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Djilali, Yasser Abdelaziz Dahou Narayan, Sanath Le Bihan, Eustache Boussaid, Haithem Almazrouei, Ebtesam Debbah, Merouane Technol Innovat Inst Abu Dhabi U Arab Emirates Dublin City Univ Dublin Ireland

ISBN: (纸本)9798350318920;9798350318937

The Lip Reading Sentences-3 (LRS3) benchmark has primarily been the focus of intense research in visual speech recognition (VSR) during the last few years. As a result, there is an increased risk of overfitting to its excessively used test set, which is only one hour duration. To alleviate this issue, we build a new VSR test set named WildVSR, by closely following the LRS3 dataset creation processes. We then evaluate and analyse the extent to which the current VSR models generalize to the new test data. We evaluate a broad range of publicly available VSR models and find significant drops in performance on our test set, compared to their corresponding LRS3 results. Our results suggest that the increase in word error rates is caused by the models' inability to generalize to slightly "harder" and in the wild lip sequences than those found in the LRS3 test set. Our new test benchmark is made public in order to enable future research towards more robust VSR models.

关键词： Algorithms Video recognition and understanding

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