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Rethinking Image Super Resolution from Long-Tailed Distribution Learning Perspective

Rethinking Image Super Resolution from Long-Tailed Distribut...

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

作者： Gou, Yuanbiao Hu, Peng Lv, Jiancheng Zhu, Hongyuan Peng, Xi Sichuan Univ Coll Comp Sci Chengdu Peoples R China ASTAR Inst Infocomm Res I2R Singapore Singapore

ISBN: (纸本)9798350301298

Existing studies have empirically observed that the resolution of the low-frequency region is easier to enhance than that of the high-frequency one. Although plentiful works have been devoted to alleviating this problem, little understanding is given to explain it. In this paper, we try to give a feasible answer from a machine learning perspective, i.e., the twin fitting problem caused by the long-tailed pixel distribution in natural images. With this explanation, we reformulate image super resolution (SR) as a long-tailed distribution learning problem and solve it by bridging the gaps of the problem between in low- and high-level vision tasks. As a result, we design a long-tailed distribution learning solution, that rebalances the gradients from the pixels in the low- and high-frequency region, by introducing a static and a learnable structure prior. The learned SR model achieves better balance on the fitting of the low- and high-frequency region so that the overall performance is improved. In the experiments, we evaluate the solution on four CNN- and one Transformer-based SR models w.r.t. six datasets and three tasks, and experimental results demonstrate its superiority.

关键词： Low-level vision

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UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentation

UCC: Uncertainty guided Cross-head Co-training for Semi-Supe...

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

作者： Fan, Jiashuo Gao, Bin Jin, Huan Jiang, Lihui Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Beijing Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep neural networks (DNNs) have witnessed great successes in semantic segmentation, which requires a large number of labeled data for training. We present a novel learning framework called Uncertainty guided Cross-head Co-training (UCC) for semi-supervised semantic segmentation. Our framework introduces weak and strong augmentations within a shared encoder to achieve co-training, which naturally combines the benefits of consistency and self-training. Every segmentation head interacts with its peers and, the weak augmentation result is used for supervising the strong. The consistency training samples' diversity can be boosted by Dynamic Cross-Set Copy-Paste (DCSCP), which also alleviates the distribution mismatch and class imbalance problems. Moreover, our proposed Uncertainty Guided Re-weight Module (UGRM) enhances the self-training pseudo labels by suppressing the effect of the low-quality pseudo labels from its peer via modeling uncertainty. Extensive experiments on Cityscapes and PAS-CAL VOC 2012 demonstrate the effectiveness of our UCC. Our approach significantly outperforms other state-of-the-art semi-supervised semantic segmentation methods. It achieves 77.17%, 76.49% mIoU on Cityscapes and PASCAL VOC 2012 datasets respectively under 1/16 protocols, which are +10.1%, +7.91% better than the supervised baseline.

关键词： Training Deep learning computer vision Uncertainty Protocols Semantics Neural networks

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Transferable Interactiveness Knowledge for Human-Object Interaction Detection 32

Transferable Interactiveness Knowledge for Human-Object Inte...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Yong-Lu Zhou, Siyuan Huang, Xijie Xu, Liang Ma, Ze Fang, Hao-Shu Wang, Yan-Feng Lu, Cewu Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn MoE Key Lab Artificial Intelligence AI Inst Shanghai Peoples R China SJTU SenseTime AI Lab Shanghai Peoples R China

ISBN: (纸本)9781728132938

Human-Object Interaction (HOI) Detection is an important problem to understand how humans interact with objects. In this paper, we explore Interactiveness Knowledge which indicates whether human and object interact with each other or not. We found that interactiveness knowledge can be learned across HOI datasets, regardless of HOI category settings. Our core idea is to exploit an Interactiveness Network to learn the general interactiveness knowledge from multiple HOI datasets and perform Non-Interaction Suppression before HOI classification in inference. On account of the generalization of interactiveness, interactiveness network is a transferable knowledge learner and can be cooperated with any HOI detection models to achieve desirable results. We extensively evaluate the proposed method on HICODET and V-COCO datasets. Our framework outperforms state-of-the-art HOI detection results by a great margin, verifying its efficacy and flexibility. Code is avail- able at https://***/DirtyHarryLYL/Transferable-Interactiveness-Network.

关键词： Action recognition Categorization recognition: Detection Retrieval

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iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis

iMiGUE: An Identity-free Video Dataset for Micro-Gesture Und...

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

作者： Liu, Xin Shi, Henglin Chen, Haoyu Yu, Zitong Li, Xiaobai Zhao, Guoying Univ Oulu Ctr Machine Vis & Signal Anal Oulu Finland Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Univ Oulu Oulu Finland

ISBN: (纸本)9781665445092

We introduce a new dataset for the emotional artificial intelligence research: identity-free video dataset for Micro-Gesture Understanding and Emotion analysis (iMiGUE). Different from existing public datasets, iMiGUE focuses on nonverbal body gestures without using any identity information, while the predominant researches of emotion analysis concern sensitive biometric data, like face and speech. Most importantly, iMiGUE focuses on micro-gestures, i.e., unintentional behaviors driven by inner feelings, which are different from ordinary scope of gestures from other gesture datasets which are mostly intentionally performed for illustrative purposes. Furthermore, iMiGUE is designed to evaluate the ability of models to analyze the emotional states by integrating information of recognized micro-gesture, rather than just recognizing prototypes in the sequences separately (or isolatedly). This is because the real need for emotion AI is to understand the emotional states behind gestures in a holistic way. Moreover, to counter for the challenge of imbalanced sample distribution of this dataset, an unsupervised learning method is proposed to capture latent representations from the micro-gesture sequences themselves. We systematically investigate representative methods on this dataset, and comprehensive experimental results reveal several interesting insights from the iMiGUE, e.g., micro-gesture-based analysis can promote emotion understanding. We confirm that the new iMiGUE dataset could advance studies of micro-gesture and emotion AI.

关键词： Emotion recognition computer vision Analytical models Face recognition Computational modeling Biological system modeling Prototypes

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Transfer Learning via Unsupervised Task Discovery for Visual Question Answering 32

Transfer Learning via Unsupervised Task Discovery for Visual...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Noh, Hyeonwoo Kim, Taehoon Mun, Jonghwan Han, Bohyung POSTECH Comp Vis Lab Pohang South Korea OpenAI San Francisco CA USA Seoul Natl Univ Comp Vis Lab ECE & ASRI Seoul South Korea Devsisters Seoul South Korea

ISBN: (纸本)9781728132938

We study how to leverage off-the-shelf visual and linguistic data to cope with out-of-vocabulary answers in visual question answering task. Existing large-scale visual datasets with annotations such as image class labels, bounding boxes and region descriptions are good sources for learning rich and diverse visual concepts. However, it is not straightforward how the visual concepts can be captured and transferred to visual question answering models due to missing link between question dependent answering models and visual data without question. We tackle this problem in two steps: 1) learning a task conditional visual classifier, which is capable of solving diverse question-specific visual recognition tasks, based on unsupervised task discovery and 2) transferring the task conditional visual classifier to visual question answering models. Specifically, we employ linguistic knowledge sources such as structured lexical database (e.g. WordNet) and visual descriptions for unsupervised task discovery, and transfer a learned task conditional visual classifier as an answering unit in a visual question answering model. We empirically show that the proposed algorithm generalizes to out-of-vocabulary answers successfully using the knowledge transferred from the visual dataset.

关键词： Deep Learning Representation Learning vision + Language

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Exploring Structured Semantic Prior for Multi Label recognition with Incomplete Labels

Exploring Structured Semantic Prior for Multi Label Recognit...

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

作者： Ding, Zixuan Wang, Ao Chen, Hui Zhang, Qiang Liu, Pengzhang Bao, Yongjun Yan, Weipeng Han, Jungong Xidian Univ Xian Peoples R China Tsinghua Univ Beijing Peoples R China BNRist Beijing Peoples R China Hangzhou Zhuoxi Inst Brain & Intelligence Hangzhou Peoples R China JD Com Beijing Peoples R China Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Univ Sheffield Ctr Machine Intelligence Sheffield S Yorkshire England

ISBN: (纸本)9798350301298

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, i.e., CLIP [22], to compensate for insufficient annotations. In spite of promising performance, they generally overlook the valuable prior about the label-to-label correspondence. In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label correspondence via a semantic prior prompter. We then present a novel Semantic Correspondence Prompt Network (SCPNet), which can thoroughly explore the structured semantic prior. A Prior-Enhanced Self-Supervised Learning method is further introduced to enhance the use of the prior. Comprehensive experiments and analyses on several widely used benchmark datasets show that our method significantly outperforms existing methods on all datasets, well demonstrating the effectiveness and the superiority of our method. Our code will be available at https://***/jameslahm/SCPNet.

关键词： detection recognition: Categorization retrieval

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Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification

Leveraging Cross-Modal Neighbor Representation for Improved ...

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

作者： Yi, Chao Ren, Lu Zhan, De-Chuan Ye, Han-Jia Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350353006

CLIP showcases exceptional cross-modal matching capabilities due to its training on image-text contrastive learning tasks. However, without specific optimization for unimodal scenarios, its performance in single-modality feature extraction might be suboptimal. Despite this, some studies have directly used CLIP's image encoder for tasks like few-shot classification, introducing a misalignment between its pre-training objectives and feature extraction methods. This inconsistency can diminish the quality of the image's feature representation, adversely affecting CLIP's effectiveness in target tasks. In this paper, we view text features as precise neighbors of image features in CLIP's space and present a novel CrOss-moDal nEighbor Representation (CODER) based on the distance structure between images and their neighbor texts. This feature extraction method aligns better with CLIP's pre-training objectives, thereby fully leveraging CLIP's robust cross-modal capabilities. The key to construct a high-quality CODER lies in how to create a vast amount of high-quality and diverse texts to match with images. We introduce the Auto Text Generator (ATG) to automatically generate the required texts in a data-free and training-free manner. We apply CODER to CLIP's zero-shot and few-shot image classification tasks. Experiment results across various datasets and models confirm CODER's effectiveness. Code is available at: https://***/YCaigogogo/cvpr24-CODER.

关键词： Multimodal Learning vision-Language Model

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Deep Structure-Revealed Network for Texture recognition

Deep Structure-Revealed Network for Texture Recognition

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

作者： Zhai, Wei Cao, Yang Zha, Zheng-Jun Xie, HaiYong Wu, Feng Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728171685

Texture recognition is a challenging visual task since various primitives along with their arrangements can be recognized from a same texture image when perceiving with different contexts. Some recent work building on CNNs exploits orderless aggregating to provide invariance to spatial arrangements. However, these methods ignore the inherent structural property of textures, which is a critical cue for distinguishing and describing texture images in the wild. To address this problem, we propose a novel Deep Structure-Revealed Network (DSR-Net) that leverages spatial dependency among the captured primitives as structural representation for texture recognition. Specifically, a primitive capturing module (PCM) is devised to generate multiple primitives from eight directional spatial contexts, in which deep features are firstly extracted under the constrains of direction map and then encoded based on the similarities of neighborhood. Next, these primitives are associated with a dependence learning module (DLM) to generate structural representation, in which a two-way collaborative relationship strategy is introduced to perceive the spatial dependencies among multiple primitives. At last, the structure-revealed texture representations are integrated with spatial ordered information to achieve real-world texture recognition. Evaluation on the five most challenging texture recognition datasets has demonstrated the superiority of the proposed model against state-of-the-art methods. The structure-revealed performances of DSR-Net are further verified on some extensive experiments, including fine-grained classification and semantic segmentation.

关键词： Textures

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Unified Transformer Tracker for Object Tracking

Unified Transformer Tracker for Object Tracking

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

作者： Ma, Fan Shou, Mike Zheng Zhu, Linchao Fan, Haoqi Xu, Yilei Yang, Yi Yan, Zhicheng Univ Technol Sydney ReLER Lab AAII Ultimo NSW Australia Natl Univ Singapore Singapore Singapore Meta AI Menlo Pk CA 94025 USA Zhejiang Univ Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

As an important area in computer vision, object tracking has formed two separate communities that respectively study Single Object Tracking (SOT) and Multiple Object Tracking (MOT). However, current methods in one tracking scenario are not easily adapted to the other due to the divergent training datasets and tracking objects of both tasks. Although UniRack [45] demonstrates that a shared appearance model with multiple heads can be used to tackle individual tracking tasks, it fails to exploit the large-scale tracking datasets for training and performs poorly on the single object tracking. In this work, we present the Unified Transformer Tracker (UTT) to address tracking problems in different scenarios with one paradigm. A track transformer is developed in our UTT to track the target in both SOT and mar where the correlation between the target feature and the tracking frame feature is exploited to localize the target. We demonstrate that both SOT and mar tasks can be solved within this framework, and the model can be simultaneously end-to-end trained by alternatively optimizing the SOT and MOT objectives on the datasets of individual tasks. Extensive experiments are conducted on several benchmarks with a unified model trained on both SOT and MOT datasets.

关键词： Training computer vision Target tracking Correlation Computational modeling Benchmark testing Transformers

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Clothes-Changing Person Re-identification with RGB Modality Only

Clothes-Changing Person Re-identification with RGB Modality ...

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

作者： Gu, Xinqian Chang, Hong Ma, Bingpeng Bai, Shutao Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The key to address clothes-changing person reidentification (re-id) is to extract clothes-irrelevant features, e.g., face, hairstyle, body shape, and gait. Most current works mainly focus on modeling body shape from multimodality information (e.g., silhouettes and sketches), but do not make full use of the clothes-irrelevant information in the original RGB images. In this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine clothes-irrelevant features from the original RGB images by penalizing the predictive power of re-id model w.r.t. clothes. Extensive experiments demonstrate that using RGB images only, CAL outperforms all state-of-the-art methods on widely-used clothes-changing person re-id benchmarks. Besides, compared with images, videos contain richer appearance and additional temporal information, which can be used to model proper spatiotemporal patterns to assist clothes-changing re-id. Since there is no publicly available clothes-changing video re-id dataset, we contribute a new dataset named CCVID and show that there exists much room for improvement in modeling spatiotemporal information.

关键词： Training computer vision Codes Shape Predictive models Feature extraction Spatiotemporal phenomena

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