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M²SGD: Learning to Learn ImportantWeights

M<SUP>2</SUP>SGD: Learning to Learn ImportantWeights

引用

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

作者： Kuo, Nicholas I-Hsien Harandi, Mehrtash Fourrier, Nicolas Walder, Christian Ferraro, Gabriela Suominen, Hanna Australian Natl Univ RSCS Canberra ACT Australia Monash Univ ECSE Clayton Vic Australia CSIRO Data61 Canberra ACT Australia Vole Univ Leonard de Vinci Paris France Univ Turku Dept Future Technol Turku Finland

ISBN: (纸本)9781728193601

Meta-learning concerns rapid knowledge acquisition. One popular approach cast optimisation as a learning problem and it has been shown that learnt neural optimisers updated base learners more quickly than their hand-crafted counterparts. In this paper, we learn an optimisation rule that sparsely updates the learner parameters and removes redundant weights. We present Masked Meta-SGD (M2SGD), a neural optimiser which is not only capable of updating learners quickly, but also capable of removing 83.71% weights for ResNet20s. We release our codes at https://***/Nic5472K/CLvision2020_cvpr_M2SGD.

关键词： computer vision

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NTIRE 2021 Challenge on Video Super-Resolution

NTIRE 2021 Challenge on Video Super-Resolution

引用

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

作者： Son, Sanghyun Lee, Suyoung Nah, Seungjun Timofte, Radu Lee, Kyoung Mu Chan, Kelvin C. K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Jiang, Boyuan Lin, Chuming Dong, Yuchun Luo, Donghao Chu, Wenqing Ji, Xiaozhong Yang, Siqian Tai, Ying Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Chengpeng Chu, Xiaojie Zhang, Jie Lu, Xin Chen, Liangyu Lin, Jing Du, Guodong Hao, Jia Zou, Xueyi Zhang, Qi Jiang, Lielin Li, Xin Zheng, He Liu, Fanglong He, Dongliang Li, Fu Dang, Qingqing Yi, Peng Wang, Zhongyuan Jiang, Kui Jiang, Junjun Ma, Jiayi Chen, Yuxiang Wang, Yutong Liu, Ting Sun, Qichao Liang, Huanwei Li, Yiming Li, Zekun Ruan, Zhubo Shang, Fanjie Guo, Chen Li, Haining Luo, Renjun Shen, Longjie Zafirouli, Kassiani Karageorgos, Konstantinos Konstantoudakis, Konstantinos Dimou, Anastasios Daras, Petros Song, Xiaowei Zhuo, Xu Liu, Hanxi Guo, Mengxi Li, Junlin Li, Yu Zhu, Ye Wang, Qing Zhao, Shijie Sun, Xiaopeng Zhan, Gen Xie, Tangxin Jia, Yu Lu, Yunhua Zhang, Wenhao Sun, Mengdi Michelini, Pablo Navarrete Zhang, Xueheng Jiang, Hao Chen, Zhiyu Chen, Li Xiong, Zhiwei Xiao, Zeyu Xu, Ruikang Cheng, Zhen Fu, Xueyang Song, Fenglong Luo, Zhipeng Yao, Yuehan Dutta, Saikat Shah, Nisarg A. Das, Sourya Dipta Zhao, Peng Shi, Yukai Liu, Hongying Shang, Fanhua Liu, Yuanyuan Chen, Fei Yu, Fangxu Gao, Ruisheng Bai, Yixin Heo, Jeonghwan Yue, Shijie Li, Chenghua Li, Jinjing Zheng, Qian Gang, Ruipeng Song, Ruixia Wee, Seungwoo Jeong, Jechang Li, Chen Wen, Geyingjie Chai, Xinning Song, Li SNU ASRI Dept ECE Seoul South Korea Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Nanyang Technol Univ S Lab Singapore Singapore Tencent YouTu Lab Shenzhen Peoples R China East China Normal Univ Shanghai Peoples R China Megvii Beijing Peoples R China Fudan Univ Shanghai Peoples R China Huawei Noahs Ark Lab Shanghai Peoples R China HiSilicon Shanghai Technol Co Ltd Shanghai Peoples R China Baidu Inc Dept Comp Vis Technol Vis Beijing Peoples R China Wuhan Univ Natl Engn Res Ctr Multimedia Software Wuhan Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Meitu Inc MTlab Xiamen Peoples R China Xidian Univ Xian Peoples R China ITI CERTH Visual Comp Lab VCL Patras Greece Southeast Univ Nanjing Peoples R China Tencent PCG Appl Res Ctr ARC Shenzhen Peoples R China South China Univ Technol Guangzhou Peoples R China ByteDance Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China China Elect Technol Cyber Secur Co Ltd Beijing Peoples R China BOE Technol Grp Co Ltd AIoT R&D Ctr Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Sci & Technol China Chengdu Peoples R China Huawei Noahs Ark Lab Beijing Peoples R China DeepBlue Technol Shanghai Co Ltd Shanghai Peoples R China Indian Inst Technol Madras Madras Tamil Nadu India Indian Inst Technol Jodhpur Jodhpur Rajasthan India Jadavpur Univ Kolkata India Hanyang Univ Seoul South Korea NCUT Beijing Peoples R China CASIA Beijing Peoples R China CUC Beijing Peoples R China NRTA Beijing Peoples R China

ISBN: (纸本)9781665448994

Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paper reviews the NTIRE 2021 Challenge on Video Super-Resolution. We present evaluation results from two competition tracks as well as the proposed solutions. Track 1 aims to develop conventional video SR methods focusing on the restoration quality. Track 2 assumes a more challenging environment with lower frame rates, casting spatio-temporal SR problem. In each competition, 247 and 223 participants have registered, respectively. During the final testing phase, 14 teams competed in each track to achieve state-of-the-art performance on video SR tasks.

关键词： computer vision Casting conferences Superresolution Focusing pattern recognition Image restoration

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Simplifying Transformations for a Family of Elastic Metrics on the Space of Surfaces

Simplifying Transformations for a Family of Elastic Metrics ...

引用

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

作者： Su, Zhe Bauer, Martin Klassen, Eric Gallivan, Kyle Florida State Univ Dept Math Tallahassee FL 32306 USA

ISBN: (纸本)9781728193601

We define a new representation for immersed surfaces in R-3 by combining the SRNF and the induced surface metric. Using the L-2 metric on the space of SRNFs and the DeWitt metric on the space of surface metrics, we obtain a 3-parameter family of metrics that corresponds to the family of "elastic metrics" proposed by Jermyn et al. in [19] on the space of immersed surfaces. Similar to the original SRNF representation this new representation results in an extrinsic distance function on the space of immersed surfaces that is easy to compute as it is given by an explicit formula. In addition to avoiding the degeneracy of the SRNF it allows for a data-driven choice of the parameters of the metric, while still providing for fast and accurate registration of surfaces.

关键词： computer vision

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Recognizing handwritten mathematical expressions via paired dual loss attention network and printed mathematical expressions

Recognizing handwritten mathematical expressions via paired ...

引用

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

作者： Anh Duc Le Ctr Open Data Humanities Tokyo Japan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

recognition of Handwritten Mathematical Expressions (HMEs) is a challenging problem because of the complicated structure and uncommon math symbols contained in HMEs. Moreover, the lack of training data is a serious issue, especially for deep learning-based systems. In this paper, we proposed a dual loss attention model that utilizes the existing latex corpus to improve accuracy. The proposed dual loss attention has two losses, including decoder loss and context matching loss to learn semantic invariant features for the encoder and latex grammar for the decoder from handwritten and printed MEs. The results of experiments on the CROHME 2014 and 2016 databases demonstrate the superiority and effectiveness of our proposed model. These results are competitive compared to others reported in recent literature.

关键词： Training Decoding Feature extraction Handwriting recognition Mathematical model Semantics Grammar

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Generalized Class Incremental Learning

Generalized Class Incremental Learning

引用

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

作者： Mi, Fei Kong, Lingjing Lin, Tao Yu, Kaicheng Faltings, Boi Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (纸本)9781728193601

Many real-world machine learning systems require the ability to continually learn new knowledge. Class incremental learning receives increasing attention recently as a solution towards this goal. However, existing methods often introduce some assumptions to simplify the problem setting, which rarely holds in real-world scenarios. In this paper, we formulate a Generalized Class Incremental Learning (GCIL) framework to systematically alleviate these restrictions, and introduce several novel realistic incremental learning scenarios. In addition, we propose a simple yet effective method, namely ReMix, which combines Exemplar Replay (ER) and Mixup to deal with different challenges in realistic GCIL setups. We demonstrate on CIFAR-100 that ReMix outperforms the state-of-the-art methods in different GCIL setups by significant margins without introducing additional computation cost.

关键词： computer vision

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VoronoiNet General Functional Approximators with Local Support

VoronoiNet General Functional Approximators with Local Suppo...

引用

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

作者： Williams, Francis Parent-Levesque, Jerome Nowrouzezahrai, Derek Panozzo, Daniele Yi, Kwang Moo Tagliasacchi, Andrea NYU New York NY 10003 USA McGill Univ Montreal PQ Canada Univ Victoria Victoria BC Canada Google Brain Mountain View CA USA

ISBN: (纸本)9781728193601

Voronoi diagrams are highly compact representations that are used in various Graphics applications. In this work, we show how to embed a differentiable version of it - via a novel deep architecture - into a generative deep network. By doing so, we achieve a highly compact latent embedding that is able to provide much more detailed reconstructions, both in 2D and 3D, for various shapes. In this tech report, we introduce our representation and present a set of preliminary results comparing it with recently proposed implicit occupancy networks.

关键词： computer vision

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Class-Balanced Training for Deep Face recognition

Class-Balanced Training for Deep Face Recognition

引用

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

作者： Zhang, Yaobin Deng, Weihong Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781728193601

The performance of deep face recognition depends heavily on the training data. Recently, larger and larger datasets have been developed for the training of deep models. However, most face recognition training sets suffer from the class imbalance problem, and most studies ignore the benefit of optimizing dataset structures. In this paper, we study how class-balanced training can promote face recognition performance. A medium-scale face recognition training set BUPT-CBFace is built by exploring the optimal data structure from massive data. This publicly available dataset is characterized by the uniformly distributed sample size per class, as well as the balance between the number of classes and the number of samples in one class. Experimental results show that deep models trained with BUPT-CBFace can not only achieve comparable results to larger-scale datasets such as MS-Celeb-1M but also alleviate the problem of recognition bias.

关键词： Face recognition

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End-to-end Optimized Video Compression with MV-Residual Prediction

End-to-end Optimized Video Compression with MV-Residual Pred...

引用

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

作者： Wu, XiangJi Zhang, Ziwen Feng, Jie Zhou, Lei Wu, Junmin Tucodec Inc Shanghai Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

We present an end-to-end trainable framework for P-frame compression in this paper. A joint motion vector (MV) and residual prediction network MV-Residual is designed to extract the ensembled features of motion representations and residual information by treating the two successive frames as inputs. The prior probability of the latent representations is modeled by a hyperprior auto-encoder and trained jointly with the MV-Residual network. Specially, the spatially-displaced convolution is applied for video frame prediction, in which a motion kernel for each pixel is learned to generate predicted pixel by applying the kernel at a displaced location in the source image. Finally, novel rate allocation and post-processing strategies are used to produce the final compressed bits, considering the bits constraint of the challenge. The experimental results on validation set show that the proposed optimized framework can generate the highest MS-SSIM for P-frame compression competition.

关键词： Image coding Video compression Convolution Kernel computer vision conferences pattern recognition

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CPARR: Category-based Proposal Analysis for Referring Relationships

CPARR: Category-based Proposal Analysis for Referring Relati...

引用

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

作者： He, Chuanzi Zhu, Haidong Gao, Jiyang Chen, Kan Nevatia, Ram Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781728193601

The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of . This requires simultaneous localization of the subject and object entities in a specified relationship. We introduce a simple yet effective proposal-based method for referring relationships. Different from the existing methods such as SSAS, our method can generate a high-resolution result while reducing its complexity and ambiguity. Our method is composed of two modules: a category-based proposal generation module to select the proposals related to the entities and a predicate analysis module to score the compatibility of pairs of selected proposals. We show state-of-the-art performance on the referring relationship task on two public datasets: Visual Relationship Detection and Visual Genome.

关键词： computer vision

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ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification

ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill ...

引用

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

作者： Usuyama, Naoto Delgado, Natalia Larios Hall, Amanda K. Lundin, Jessica Microsoft Healthcare Redmond WA 98052 USA Microsoft Redmond WA USA

ISBN: (纸本)9781728193601

Identifying prescription medications is a frequent task for patients and medical professionals;however, this is an error-prone task as many pills have similar appearances (e.g. white round pills), which increases the risk of medication errors. In this paper, we introduce ePillID, the largest public benchmark on pill image recognition, composed of 13k images representing 8184 appearance classes (two sides for 4092 pill types). For most of the appearance classes, there exists only one reference image, making it a challenging low-shot recognition setting. We present our experimental setup and evaluation results of various baseline models on the benchmark. The best baseline using a multi-head metric-learning approach with bilinear features performed remarkably well;however, our error analysis suggests that they still fail to distinguish particularly confusing classes.

关键词： Image recognition

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