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CHMATCH: Contrastive Hierarchical Matching and Robust Adaptive Threshold Boosted Semi-Supervised Learning

CHMATCH: Contrastive Hierarchical Matching and Robust Adapti...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jianlong Wu Haozhe Yang Tian Gan Ning Ding Feijun Jiang Liqiang Nie School of Computer Science and Technology Harbin Institute of Technology Shenzhen School of Computer Science and Technology Shandong University Alibaba Group

The recently proposed FixMatch and FlexMatch have achieved remarkable results in the field of semi-supervised learning. But these two methods go to two extremes as FixMatch and FlexMatch use a pre-defined constant threshold for all classes and an adaptive threshold for each category, respectively. By only investigating consistency regularization, they also suffer from unstable results and indiscriminative feature representation, especially under the situation of few labeled samples. In this paper, we propose a novel CHMatch method, which can learn robust adaptive thresholds for instance-level prediction matching as well as discriminative features by contrastive hierarchical matching. We first present a memory-bank based robust threshold learning strategy to select highly-confident samples. In the meantime, we make full use of the structured information in the hierarchical labels to learn an accurate affinity graph for contrastive learning. CHMatch achieves very stable and superior results on several commonly-used benchmarks. For example, CHMatch achieves 8.44% and 9.02% error rate reduction over FlexMatch on CIFAR-100 under WRN-28-2 with only 4 and 25 labeled samples per class, respectively 1 1 Project address: https://***/sailist/CHMatch.

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Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts

Exploring Data-Efficient 3D Scene Understanding with Contras...

引用

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

作者： Hou, Ji Graham, Benjamin Niesner, Matthias Xie, Saining Tech Univ Munich Munich Germany Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

The rapid progress in 3D scene understanding has come with growing demand for data;however, collecting and annotating 3D scenes (e.g. point clouds) are notoriously hard. For example, the number of scenes (e.g. indoor rooms) that can be accessed and scanned might be limited;even given sufficient data, acquiring 3D labels (e.g. instance masks) requires intensive human labor. In this paper, we explore data-efficient learning for 3D point cloud. As a first step towards this direction, we propose Contrastive Scene Contexts, a 3D pre-training method that makes use of both point-level correspondences and spatial contexts in a scene. Our method achieves state-of-the-art results on a suite of benchmarks where training data or labels are scarce. Our study reveals that exhaustive labelling of 3D point clouds might be unnecessary;and remarkably, on ScanNet, even using 0.1% of point labels, we still achieve 89% (instance segmentation) and 96% (semantic segmentation) of the baseline performance that uses full annotations.

关键词： computer vision Three-dimensional displays Annotations Semantics Training data Benchmark testing pattern recognition

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Mutual Support of Data Modalities in the Task of Sign Language recognition

Mutual Support of Data Modalities in the Task of Sign Langua...

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

作者： Gruber, Ivan Krnoul, Zdenek Hruz, Marek Kanis, Jakub Bohacek, Matyas Univ West Bohemia Fac Appl Sci Dept Cybernet & New Technol Informat Soc Tech 8 Plzen 30100 Czech Republic Gymnasium Jan Kepler Parlerova 2-118 Prague 16900 6 Czech Republic

ISBN: (纸本)9781665448994

This paper presents a method for automatic sign language recognition that was utilized in the cvpr 2021 ChaLearn Challenge (RGB track). Our method is composed of several approaches combined in an ensemble scheme to perform isolated sign-gesture recognition. We combine modalities of video sample frames processed by a 3D ConvNet (I3D), with body-pose information in the form of joint locations processed by a Transformer, hand region images transformed into a semantic space, and linguistically defined locations of hands. Although the individual models perform sub-par (60% to 93% accuracy on validation data), the weighted ensemble results in 95.46% accuracy.

关键词： Visualization Three-dimensional displays Assistive technology Computational modeling conferences Semantics Gesture recognition

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pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis

pi-GAN: Periodic Implicit Generative Adversarial Networks fo...

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

作者： Chan, Eric R. Monteiro, Marco Kellnhofer, Petr Wu, Jiajun Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches however fall short in two ways: first, they may lack an underlying 3D representation or rely on view-inconsistent rendering, hence synthesizing images that are not multi-view consistent;second, they often depend upon representation network architectures that are not expressive enough, and their results thus lack in image quality. We propose a novel generative model, named Periodic Implicit Generative Adversarial Networks (pi-GAN or pi-GAN), for high-quality 3D-aware image synthesis. pi-GAN leverages neural representations with periodic activation functions and volumetric rendering to represent scenes as view-consistent radiance fields. The proposed approach obtains state-of-the-art results for 3D-aware image synthesis with multiple real and synthetic datasets.

关键词： Image quality Visualization computer vision Three-dimensional displays Image synthesis Network architecture Rendering (computer graphics)

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Learning Graphs for Knowledge Transfer with Limited Labels

Learning Graphs for Knowledge Transfer with Limited Labels

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

作者： Ghosh, Pallabi Saini, Nirat Davis, Larry S. Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665445092

Fixed input graphs are a mainstay in approaches that utilize Graph Convolution Networks (GCNs) for knowledge transfer. The standard paradigm is to utilize relationships in the input graph to transfer information using GCNs from training to testing nodes in the graph;for example, the semi-supervised, zero-shot, and few-shot learning setups. We propose a generalized framework for learning and improving the input graph as part of the standard GCN-based learning setup. Moreover, we use additional constraints between similar and dissimilar neighbors for each node in the graph by applying triplet loss on the intermediate layer output. We present results of semi-supervised learning on Citeseer, Cora, and Pubmed benchmarking datasets, and zero/few-shot action recognition on UCF101 and HMDB51 datasets, significantly outperforming current approaches. We also present qualitative results visualizing the graph connections that our approach learns to update.

关键词： Training Visualization computer vision Convolution Semisupervised learning Benchmark testing pattern recognition

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Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction

Bilevel Online Adaptation for Out-of-Domain Human Mesh Recon...

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

作者： Guan, Shanyan Xu, Jingwei Wang, Yunbo Ni, Bingbing Yang, Xiaokang Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

This paper considers a new problem of adapting a pretrained model of human mesh reconstruction to out-of-domain streaming videos. However, most previous methods based on the parametric SMPL model [36] underperform in new domains with unexpected, domain-specific attributes, such as camera parameters, lengths of bones, backgrounds, and occlusions. Our general idea is to dynamically fine-tune the source model on test video streams with additional temporal constraints, such that it can mitigate the domain gaps without over-fitting the 2D information of individual test frames. A subsequent challenge is how to avoid conflicts between the 2D and temporal constraints. We propose to tackle this problem using a new training algorithm named Bilevel Online Adaptation (BOA), which divides the optimization process of overall multi-objective into two steps of weight probe and weight update in a training iteration. We demonstrate that BOA leads to state-of-the-art results on two human mesh reconstruction benchmarks(1).

关键词： Training Adaptation models computer vision Benchmark testing Reconstruction algorithms Cameras pattern recognition

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Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression recognition

Feature Representation Learning with Adaptive Displacement G...

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conference on computer vision and pattern recognition (cvpr)

作者： Zhijun Zhai Jianhui Zhao Chengjiang Long Wenju Xu Shuangjiang He Huijuan Zhao School of Computer Science Wuhan University Wuhan Hubei China Meta Reality Labs Burlingame CA USA OPPO US Research Center InnoPeak Technology Inc Palo Alto CA USA FiberHome Telecommunication Technologies Co. Ltd Wuhan Hubei China

Micro-expressions are spontaneous, rapid and subtle facial movements that can neither be forged nor suppressed. They are very important nonverbal communication clues, but are transient and of low intensity thus difficult to recognize. Recently deep learning based methods have been developed for micro-expression (ME) recognition using feature extraction and fusion techniques, however, targeted feature learning and efficient feature fusion still lack further study according to the ME characteristics. To address these issues, we propose a novel framework Feature Representation Learning with adaptive Displacement Generation and Transformer fusion (FRL-DGT), in which a convolutional Displacement Generation Module (DGM) with self-supervised learning is used to extract dynamic features from onset/apex frames targeted to the subsequent ME recognition task, and a well-designed Transformer Fusion mechanism composed of three Transformer-based fusion modules (local, global fusions based on AU regions and full-face fusion) is applied to extract the multi-level informative features after DGM for the final ME prediction. The extensive experiments with solid leave-one-subject-out (LOSO) evaluation results have demonstrated the superiority of our proposed FRL-DGT to state-of-the-art methods.

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LQF: Linear Quadratic Fine-Tuning

LQF: Linear Quadratic Fine-Tuning

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

作者： Achille, Alessandro Golatkar, Aditya Ravichandran, Avinash Polito, Marzia Soatto, Stefano Amazon Web Serv Seattle WA 98109 USA Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781665445092

Classifiers that are linear in their parameters, and trained by optimizing a convex loss function, have predictable behavior with respect to changes in the training data, initial conditions, and optimization. Such desirable properties are absent in deep neural networks (DNNs), typically trained by non-linear fine-tuning of a pre-trained model. Previous attempts to linearize DNNs have led to interesting theoretical insights, but have not impacted the practice due to the substantial performance gap compared to standard non-linear optimization. We present the first method for linearizing a pre-trained model that achieves comparable performance to non-linear fine-tuning on most of real-world image classification tasks tested, thus enjoying the interpretability of linear models without incurring punishing losses in performance. LQF consists of simple modifications to the architecture, loss function and optimization typically used for classification: Leaky-ReLU instead of ReLU, mean squared loss instead of cross-entropy, and pre-conditioning using Kronecker factorization. None of these changes in isolation is sufficient to approach the performance of non-linear fine-tuning. When used in combination, they allow us to reach comparable performance, and even superior in the low-data regime, while enjoying the simplicity, robustness and interpretability of linear-quadratic optimization.

关键词： Deep learning computer vision Training data computer architecture Robustness pattern recognition Task analysis

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Fine-Grained Classification with Noisy Labels

Fine-Grained Classification with Noisy Labels

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conference on computer vision and pattern recognition (cvpr)

作者： Qi Wei Lei Feng Haoliang Sun Ren Wang Chenhui Guo Yilong Yin School of Software Shandong University China School of Computer Science and Engineering Nanyang Technological University Singapore

Learning with noisy labels (LNL) aims to ensure model generalization given a label-corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine-grained datasets (LNL-FG), which is more practical and challenging as large inter-class ambiguities among fine-grained classes cause more noisy labels. We empirically show that existing methods that work well for LNL fail to achieve satisfying performance for LNL-FG, arising the practical need of effective solutions for LNL-FG. To this end, we propose a novel framework called stochastic noise-tolerated supervised contrastive learning (SNSCL) that confronts label noise by encouraging distinguishable representation. Specifically, we design a noise-tolerated supervised contrastive learning loss that incorporates a weight-aware mechanism for noisy label correction and selectively updating momentum queue lists. By this mechanism, we mitigate the effects of noisy anchors and avoid inserting noisy labels into the momentum-updated queue. Besides, to avoid manually-defined augmentation strategies in contrastive learning, we propose an efficient stochastic module that samples feature embeddings from a generated distribution, which can also enhance the representation ability of deep models. SNSCL is general and compatible with prevailing robust LNL strategies to improve their performance for LNL-FG. Extensive experiments demonstrate the effectiveness of SNSCL.

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Spatially Consistent Representation Learning

Spatially Consistent Representation Learning

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

作者： Roh, Byungseok Shin, Wuhyun Kim, Ildoo Kim, Sungwoong Kakao Brain Seoul South Korea

ISBN: (纸本)9781665445092

Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive performances on downstream image classification tasks. While these contrastive methods mainly focus on generating invariant global representations at the image-level under semantic-preserving transformations, they are prone to overlook spatial consistency of local representations and therefore have a limitation in pretraining for localization tasks such as object detection and instance segmentation. Moreover, aggressively cropped views used in existing contrastive methods can minimize representation distances between the semantically different regions of a single image. In this paper, we propose a spatially consistent representation learning algorithm (SCRL) for multi-object and location-specific tasks. In particular, we devise a novel self-supervised objective that tries to produce coherent spatial representations of a randomly cropped local region according to geometric translations and zooming operations. On various downstream localization tasks with benchmark datasets, the proposed SCRL shows significant performance improvements over the image-level supervised pretraining as well as the state-of-the-art self-supervised learning methods.

关键词： Location awareness Learning systems Image segmentation computer vision Codes Object detection Benchmark testing

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