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Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification

Contrastive Learning based Hybrid Networks for Long-Tailed I...

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

作者： Wang, Peng Han, Kai Wei, Xiu-Shen Zhang, Lei Wang, Lei Univ Wollongong Wollongong NSW Australia Univ Bristol Bristol Avon England Nanjing Univ Sci & Technol Nanjing Peoples R China Northwestern Polytech Univ Xian Peoples R China

ISBN: (纸本)9781665445092

Learning discriminative image representations plays a vital role in long-tailed image classification because it can ease the classifier learning in imbalanced cases. Given the promising performance contrastive learning has shown recently in representation learning, in this work, we explore effective supervised contrastive learning strategies and tailor them to learn better image representations from imbalanced data in order to boost the classification accuracy thereon. Specifically, we propose a novel hybrid network structure being composed of a supervised contrastive loss to learn image representations and a cross-entropy loss to learn classifiers, where the learning is progressively transited from feature learning to the classifier learning to embody the idea that better features make better classifiers. We explore two variants of contrastive loss for feature learning, which vary in the forms but share a common idea of pulling the samples from the same class together in the normalized embedding space and pushing the samples from different classes apart. One of them is the recently proposed supervised contrastive (SC) loss, which is designed on top of the state-of-the-art unsupervised contrastive loss by incorporating positive samples from the same class. The other is a prototypical supervised contrastive (PSC) learning strategy which addresses the intensive memory consumption in standard SC loss and thus shows more promise under limited memory budget. Extensive experiments on three long-tailed classification datasets demonstrate the advantage of the proposed contrastive learning based hybrid networks in long-tailed classification.

关键词： Training computer vision Memory management Graphics processing units Image representation pattern recognition Proposals

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Joint Intensity and Spatial Metric Learning for Robust Gait recognition 30

Joint Intensity and Spatial Metric Learning for Robust Gait ...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Makihara, Yasushi Suzuki, Atsuyuki Muramatsu, Daigo Li, Xiang Yagi, Yasushi Osaka Univ Suita Osaka 565 Japan Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538604571

This paper describes a joint intensity metric learning method to improve the robustness of gait recognition with silhouette-based descriptors such as gait energy images. Because existing methods often use the difference of image intensities between a matching pair (e.g., the absolute difference of gait energies for the l(1)-norm) to measure a dissimilarity, large intrasubject differences derived from covariate conditions (e.g., large gait energies caused by carried objects vs. small gait energies caused by the background), may wash out subtle intersubject differences (e.g., the difference of middle-level gait energies derived from motion differences). We therefore introduce a metric on joint intensity to mitigate the large intrasubject differences as well as leverage the subtle intersubject differences. More specifically, we formulate the joint intensity and spatial metric learning in a unified framework and alternately optimize it by linear or ranking support vector machines. Experiments using the OU-ISIR treadmill data set B with the largest clothing variation and large population data set with bag, beta version containing carrying status in the wild demonstrate the effectiveness of the proposed method.

关键词： Measurement Gait recognition Robustness Clothing Histograms Support vector machines Probes

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Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual recognition Problems

Learning Common Rationale to Improve Self-Supervised Represe...

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

作者： Shu, Yangyang van den Hengel, Anton Liu, Lingqiao Univ Adelaide Sch Comp Sci Adelaide SA Australia

ISBN: (纸本)9798350301298

Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for optimizing SSL objectives are not suitable for characterizing the subtle differences in FGVR. To overcome this issue, we propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes, dubbed as common rationales in this paper. Intuitively, common rationales tend to correspond to the discriminative patterns from the key parts of foreground objects. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective without using any pre-trained object parts or saliency detectors, making it seamlessly to be integrated with the existing SSL process. Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns. At the test stage, the branch generates a set of spatial weights to selectively aggregate features representing an instance. Extensive experimental results on four visual tasks demonstrate that the proposed method can lead to a significant improvement in different evaluation settings.(1)

关键词： Self-supervised or unsupervised representation learning

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Deep Hashing Network for Unsupervised Domain Adaptation 30

Deep Hashing Network for Unsupervised Domain Adaptation

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Venkateswara, Hemanth Eusebio, Jose Chakraborty, Shayok Panchanathan, Sethuraman Arizona State Univ Ctr Cognit Ubiquitous Comp Tempe AZ 85287 USA

ISBN: (纸本)9781538604571

In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.

关键词： Neural networks Adaptation models Machine learning computer vision Training Data models

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Temporally Efficient vision Transformer for Video Instance Segmentation

Temporally Efficient Vision Transformer for Video Instance S...

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

作者： Yang, Shusheng Wang, Xinggang Li, Yu Fang, Yuxin Fang, Jiemin Liu, Wenyu Zhao, Xun Shan, Ying Huazhong Univ Sci & Technol Sch EIC Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan Hubei Peoples R China Tencent PCG Appl Res Ctr ARC London England Int Digital Econ Acad IDEA Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently vision transformer has achieved tremendous success on image-level visual recognition tasks. To effectively and efficiently model the crucial temporal information within a video clip, we propose a Temporally Efficient vision Transformer (TeViT) for video instance segmentation (VIS). Different from previous transformer-based VIS methods, TeViT is nearly convolution-free, which contains a transformer backbone and a query-based video instance segmentation head. In the backbone stage, we propose a nearly parameter-free messenger shift mechanism for early temporal context fusion. In the head stages, we propose a parameter-shared spatiotemporal query interaction mechanism to build the one-to-one correspondence between video instances and queries. Thus, TeViT fully utilizes both frame-level and instance-level temporal context information and obtains strong temporal modeling capacity with negligible extra computational cost. On three widely adopted VIS benchmarks, i.e., YouTube-VIS-2019, YouTube-VIS-2021, and OVIS, TeViT obtains state-of-the-art results and maintains high inference speed, e.g., 46.6 AP with 68.9 FPS on YouTube-VIS-2019. Code is available at https://***/hustvl/TeViT.

关键词： Image segmentation Visualization Head Shape Transformers Spatiotemporal phenomena Computational efficiency

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Strip Pooling: Rethinking Spatial Pooling for Scene Parsing

Strip Pooling: Rethinking Spatial Pooling for Scene Parsing

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

作者： Hou, Qibin Zhang, Li Cheng, Ming-Ming Feng, Jiashi Natl Univ Singapore Singapore Singapore Univ Oxford Oxford England Nankai Univ CS Tianjin Peoples R China

ISBN: (纸本)9781728171685

Spatial pooling has been proven highly effective in capturing long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conventional spatial pooling that usually has a regular shape of N x N, we rethink the formulation of spatial pooling by introducing a new pooling strategy, called strip pooling, which considers a long but narrow kernel, i.e., 1 x N or N x 1. Based on strip pooling, we further investigate spatial pooling architecture design by I) introducing a new strip pooling module that enables backbone networks to efficiently model long-range dependencies, 2) presenting a novel building block with diverse spatial pooling as a core, and 3) systematically comparing the performance of the proposed strip pooling and conventional spatial pooling techniques. Both novel pooling-based designs are lightweight and can serve as an efficient plugand-play module in existing scene parsing networks. Extensive experiments on popular benchmarks (e.g., ADE20K and Cityscapes) demonstrate that our simple approach establishes new state-of-the-art results. Code is available at https://***/Andrew-Qibin/SPNet.

关键词： computer vision

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FocusCut: Diving into a Focus View in Interactive Segmentation

FocusCut: Diving into a Focus View in Interactive Segmentati...

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

作者： Lin, Zheng Duan, Zheng-Peng Zhang, Zhao Guo, Chun-Le Cheng, Ming-Ming Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Interactive image segmentation is an essential tool in pixel-level annotation and image editing. To obtain a high-precision binary segmentation mask, users tend to add interaction clicks around the object details, such as edges and holes, for efficient refinement. Current methods regard these repair clicks as the guidance to jointly determine the global prediction. However, the global view makes the model lose focus from later clicks, and is not in line with user intentions. In this paper, we dive into the view of clicks' eyes to endow them with the decisive role in object details again. To verify the necessity of focus view, we design a simple yet effective pipeline, named FocusCut, which integrates the functions of object segmentation and local refinement. After obtaining the global prediction, it crops click-centered patches from the original image with adaptive scopes to refine the local predictions progressively. Without user perception and parameters increase, our method has achieved state-of-the-art results. Extensive experiments and visualized results demonstrate that FocusCut makes hyper fine segmentation possible for interactive image segmentation.

关键词： Image segmentation Visualization computer vision Annotations Image edge detection Pipelines Crops

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PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery

PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationshi...

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

作者： Zhang, Tianyi Lin, Jie Hu, Peng Zhao, Bin Aly, Mohamed M. Sabry ASTAR I2R Singapore Singapore Sichuan Univ Chengdu Peoples R China ASTAR IME Singapore Singapore Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665445092

Non-maximum Suppression (NMS) is an essential post-processing step in modern convolutional neural networks for object detection. Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional object detection pipelines. MaxpoolNMS is introduced as a parallelizable alternative to GreedyNMS, which in turn enables faster speed than GreedyNMS at comparable accuracy. However, MaxpoolNMS is only capable of replacing the GreedyNMS at the first stage of two-stage detectors like Faster-RCNN. There is a significant drop in accuracy when applying MaxpoolNMS at the final detection stage, due to the fact that MaxpoolNMS fails to approximate GreedyNMS precisely in terms of bounding box selection. In this paper, we propose a general, parallelizable and configurable approach PSRR-MaxpoolNMS, to completely replace GreedyNMS at all stages in all detectors. By introducing a simple Relationship Recovery module and a Pyramid Shifted MaxpoolNMS module, our PSRR-MaxpoolNMS is able to approximate GreedyNMS more precisely than MaxpoolNMS. Comprehensive experiments show that our approach outperforms MaxpoolNMS by a large margin, and it is proven faster than GreedyNMS with comparable accuracy. For the first time, PSRR-MaxpoolNMS provides a fully parallelizable solution for customized hardware design, which can be reused for accelerating NMS everywhere.

关键词： computer vision Pipelines Detectors Object detection Hardware pattern recognition Convolutional neural networks

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Strengthen Learning Tolerance for Weakly Supervised Object Localization

Strengthen Learning Tolerance for Weakly Supervised Object L...

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

作者： Guo, Guangyu Han, Junwei Wan, Fang Zhang, Dingwen Northwestern Polytech Univ Brain & Artificial Intelligence Lab Xian Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781665445092

Weakly supervised object localization (WSOL) aims at learning to localize objects of interest by only using the image-level labels as the supervision. While numerous efforts have been made in this field, recent approaches still suffer from two challenges: one is the part domination issue while the other is the learning robustness issue. Specifically, the former makes the localizer prone to the local discriminative object regions rather than the desired whole object, and the latter makes the localizer over-sensitive to the variations of the input images so that one can hardly obtain localization results robust to the arbitrary visual stimulus. To solve these issues, we propose a novel framework to strengthen the learning tolerance, referred to as SLT-Net, for WSOL. Specifically, we consider two fold learning tolerance strengthening mechanisms. One is the semantic tolerance strengthening mechanism, which allows the localizer to make mistakes for classifying similar semantics so that it will not concentrate too much on the discriminative local regions. The other is the visual stimuli tolerance strengthening mechanism, which enforces the localizer to be robust to different image transformations so that the prediction quality will not be sensitive to each specific input image. Finally, we implement comprehensive experimental comparisons on two widely-used datasets CUB and ILSVRC2012, which demonstrate the effectiveness of our proposed approach.

关键词： Location awareness Visualization computer vision Computational modeling Semantics Robustness pattern recognition

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FGN: Fully Guided Network for Few-Shot Instance Segmentation

FGN: Fully Guided Network for Few-Shot Instance Segmentation

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

作者： Fan, Zhibo Yu, Jin-Gang Liang, Zhihao Ou, Jiarong Gao, Changxin Xia, Gui-Song Li, Yuanqing South China Univ Technol Guangzhou Peoples R China Guangzhou Lab Guangzhou Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Wuhan Univ Wuhan Peoples R China

ISBN: (纸本)9781728171685

Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with general instance segmentation, which provides a possible way of tackling instance segmentation in the lack of abundant labeled data for training. This paper presents a Fully Guided Network (FGN) for few-shot instance segmentation. FGN perceives FSIS as a guided model where a so-called support set is encoded and utilized to guide the predictions of a base instance segmentation network (i.e., Mask R-CNN), critical to which is the guidance mechanism. In this view, FGN introduces different guidance mechanisms into the various key components in Mask R-CNN, including Attention-Guided RPN, Relation-Guided Detector, and Attention-Guided FCN, in order to make full use of the guidance effect from the support set and adapt better to the inter-class generalization. Experiments on public datasets demonstrate that our proposed FGN can outperform the state-of-the-art methods.

关键词： computer vision

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