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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual recognition 32

MetaCleaner: Learning to Hallucinate Clean Representations f...

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Weihe Wang, Yali Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab Shenzhen Key Lab Comp Vis & Pattern Recognit Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Deep Neural Networks (DNNs) have achieved remarkable successes in large-scale visual recognition. However, they often suffer from overfitting under noisy labels. To alleviate this problem, we propose a conceptually simple but effective MetaCleaner, which can learn to hallucinate a clean representation of an object category, according to a small noisy subset from the same category. Specially, MetaCleaner consists of two flexible submodules. The first submodule, namely Noisy Weighting, can estimate the confidence scores of all the images in the noisy subset, by analyzing their deep features jointly. The second submodule, namely Clean Hallucinating, can generate a clean representation from the noisy subset, by summarizing the noisy images with their confidence scores. Via MetaCleaner, DNNs can strengthen its robustness to noisy labels, as well as enhance its generalization capacity with richer data diversity. Moreover, MetaCleaner can be easily integrated into the standard training procedure of DNNs, which promotes its value for real-life applications. We conduct extensive experiments on two popular benchmarks in noisy-labeled recognition, i.e., Food-101N and Clothing1M. For both datasets, our MetaCleaner significantly outperforms baselines, and achieves the state-of-the-art performance.

关键词： Categorization Deep Learning recognition: Detection Retrieval

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Uformer: A General U-Shaped Transformer for Image Restoration

Uformer: A General U-Shaped Transformer for Image Restoratio...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Zhendong Cun, Xiaodong Bao, Jianmin Zhou, Wengang Liu, Jianzhuang Li, Houqiang Univ Sci & Technol China Hefei Peoples R China Univ Macau Zhuhai Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (纸本)9781665469463

In this paper, we present Uformer, an effective and efficient Transformer-based architecture for image restoration, in which we build a hierarchical encoder-decoder network using the Transformer block. In Uformer, there are two core designs. First, we introduce a novel locally-enhanced window (LeWin) Transformer block, which performs non-overlapping window-based self-attention instead of global self-attention. It significantly reduces the computational complexity on high resolution feature map while capturing local context. Second, we propose a learnable multi-scale restoration modulator in the form of a multi-scale spatial bias to adjust features in multiple layers of the Uformer decoder. Our modulator demonstrates superior capability for restoring details for various image restoration tasks while introducing marginal extra parameters and computational cost. Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration. To evaluate our approach, extensive experiments are conducted on several image restoration tasks, including image denoising, motion deblurring, defocus deblurring and deraining. Without bells and whistles, our Uformer achieves superior or comparable performance compared with the state-of-the-art algorithms. The code and models are available at https://***/ZhendongWang6/Uformer.

关键词： computer vision Modulation Transformer cores Transformers Image restoration pattern recognition Decoding

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Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang Yifan Wu, Shihao Oztireli, Cengiz Sorkine-Hornung, Olga Swiss Fed Inst Technol Zurich Switzerland Univ Cambridge Cambridge England

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Neural implicit functions have emerged as a powerful representation for surfaces in 3D. Such a function can encode a high quality surface with intricate details into the parameters of a deep neural network. However, optimizing for the parameters for accurate and robust reconstructions remains a challenge, especially when the input data is noisy or incomplete. In this work, we develop a hybrid neural surface representation that allows us to impose geometry-aware sampling and regularization, which significantly improves the fidelity of reconstructions. We propose to use iso-points as an explicit representation for a neural implicit function. These points are computed and updated on-the-fly during training to capture important geometric features and impose geometric constraints on the optimization. We demonstrate that our method can be adopted to improve state-of-the-art techniques for reconstructing neural implicit surfaces from multi-view images or point clouds. Quantitative and qualitative evaluations show that, compared with existing sampling and optimization methods, our approach allows faster convergence, better generalization, and accurate recovery of details and topology.

关键词： Training Deep learning Surface reconstruction Three-dimensional displays Optimization methods Topology pattern recognition

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Tackling the Ill-Posedness of Super-Resolution through Adaptive Target Generation

Tackling the Ill-Posedness of Super-Resolution through Adapt...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jo, Younghyun Oh, Seoung Wug Vajda, Peter Kim, Seon Joo Yonsei Univ Seoul South Korea Adobe Res San Jose CA USA Facebook Menlo Pk CA USA

ISBN: (纸本)9781665445092

By the one-to-many nature of the super-resolution (SR) problem, a single low-resolution (LR) image can be mapped to many high-resolution (HR) images. However, learning based SR algorithms are trained to map an LR image to the corresponding ground truth (GT) HR image in the training dataset. The training loss will increase and penalize the algorithm when the output does not exactly match the GT target, even when the outputs are mathematically valid candidates according to the SR framework. This becomes more problematic for the blind SR, as diverse unknown blur kernels exacerbate the ill-posedness of the problem. To this end, we propose a fundamentally different approach for the SR by introducing the concept of the adaptive target. The adaptive target is generated from the original GT target by a transformation to match the output of the SR network. The adaptive target provides an effective way for the SR algorithm to deal with the ill-posed nature of the SR, by providing the algorithm with the flexibility of accepting a variety of valid solutions. Experimental results show the effectiveness of our algorithm, especially for improving the perceptual quality of HR outputs.

关键词： Training computer vision Adaptive systems Impedance matching Superresolution Training data Network architecture

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Harmony Potentials for Joint Classification and Segmentation

Harmony Potentials for Joint Classification and Segmentation

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23rd IEEE conference on computer vision and pattern recognition (CVPR)

作者： Gonfaus, Josep M. Boix, Xavier van de Weijer, Joost Bagdanov, Andrew D. Serrat, Joan Gonzalez, Jordi Centre de Visió Per Computador Universitat Autònoma de Barcelona Spain Dept. of Computer Science Universitat Autònoma de Barcelona Spain

ISBN: (纸本)9781424469840

Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21.

关键词： Random processes

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Category Contrast for Unsupervised Domain Adaptation in Visual Tasks

Category Contrast for Unsupervised Domain Adaptation in Visu...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Huang, Jiaxing Guan, Dayan Xiao, Aoran Lu, Shijian Shao, Ling Nanyang Technol Univ Singapore Singapore Terminus Grp Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Instance contrast for unsupervised representation learning has achieved great success in recent years. In this work, we explore the idea of instance contrastive learning in unsupervised domain adaptation (UDA) and propose a novel Category Contrast technique (CaCo) that introduces semantic priors on top of instance discrimination for visual UDA tasks. By considering instance contrastive learning as a dictionary look-up operation, we construct a semantics-aware dictionary with samples from both source and target domains where each target sample is assigned a (pseudo) category label based on the category priors of source samples. This allows category contrastive learning (between target queries and the category-level dictionary) for category-discriminative yet domain-invariant feature representations: samples of the same category (from either source or target domain) are pulled closer while those of different categories are pushed apart simultaneously. Extensive UDA experiments in multiple visual tasks (e.g., segmentation, classification and detection) show that CaCo achieves superior performance as compared with state-of-the-art methods. The experiments also demonstrate that CaCo is complementary to existing UDA methods and generalizable to other learning setups such as unsupervised model adaptation, open-/partial-set adaptation etc.

关键词： Representation learning Visualization Adaptation models computer vision Dictionaries Computational modeling Semantics

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Adaptive Prototype Learning and Allocation for Few-Shot Segmentation

Adaptive Prototype Learning and Allocation for Few-Shot Segm...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Gen Jampani, Varun Sevilla-Lara, Laura Sun, Deqing Kim, Jonghyun Kim, Joongkyu Univ Edinburgh Edinburgh Midlothian Scotland Google Res Mountain View CA USA Sungkyunkwan Univ Seoul South Korea

ISBN: (纸本)9781665445092

Prototype learning is extensively used for few-shot segmentation. Typically, a single prototype is obtained from the support feature by averaging the global object information. However, using one prototype to represent all the information may lead to ambiguities. In this paper, we propose two novel modules, named superpixel-guided clustering (SGC) and guided prototype allocation (GPA), for multiple prototype extraction and allocation. Specifically, SGC is a parameter-free and training free approach, which extracts more representative prototypes by aggregating similar feature vectors, while GPA is able to select matched prototypes to provide more accurate guidance. By integrating the SGC and GPA together, we propose the Adaptive Superpixel-guided Network (ASGNet), which is a lightweight model and adapts to object scale and shape variation. In addition, our network can easily generalize to k-shot segmentation with substantial improvement and no additional computational cost. In particular, our evaluations on COCO demonstrate that ASGNet surpasses the state-of-the-art method by 5% in 5-shot segmentation.(1)

关键词： Image segmentation Adaptation models computer vision Shape Computational modeling Prototypes Feature extraction

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Weakly Supervised Temporal Sentence Grounding with Gaussian-based Contrastive Proposal Learning

Weakly Supervised Temporal Sentence Grounding with Gaussian-...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zheng, Minghang Huang, Yanjie Chen, Qingchao Peng, Yuxin Liu, Yang Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Peking Univ Natl Inst Hlth Data Sci Beijing Peoples R China Beijing Inst Gen Artificial Intelligence Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Temporal sentence grounding aims to detect the most salient moment corresponding to the natural language query from untrimmed videos. As labeling the temporal boundaries is labor-intensive and subjective, the weakly-supervised methods have recently received increasing attention. Most of the existing weakly-supervised methods generate the proposals by sliding windows, which are content-independent and of low quality. Moreover, they train their model to distinguish positive visual-language pairs from negative ones randomly collected from other videos, ignoring the highly confusing video segments within the same video. In this paper, we propose Contrastive Proposal Learning(CPL) to overcome the above limitations. Specifically, we use multiple learnable Gaussian functions to generate both positive and negative proposals within the same video that can characterize the multiple events in a long video. Then, we propose a controllable easy to hard negative proposal mining strategy to collect negative samples within the same video, which can ease the model optimization and enables CPL to distinguish highly confusing scenes. The experiments show that our method achieves state-of-the-art performance on Charades-STA and ActivityNet Captions datasets. The code and models are available at https://***/minghangz/cpl.

关键词： Training Location awareness computer vision Codes Grounding Natural languages pattern recognition

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Interpolation-based Semi-supervised Learning for Object Detection

Interpolation-based Semi-supervised Learning for Object Dete...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jeong, Jisoo Verma, Vikas Hyun, Minsung Kannala, Juho Kwak, Nojun Seou1 Natl Univ Seoul South Korea Aalto Univ Espoo Finland Mila Quebec Artificial Intelligence Inst Montreal PQ Canada SK Hynix Icheon Si South Korea

ISBN: (纸本)9781665445092

Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection have not been studied much. In this paper, we propose an Interpolation-based Semi-supervised learning method for object Detection (ISD), which considers and solves the problems caused by applying conventional Interpolation Regularization (IR) directly to object detection. We divide the output of the model into two types according to the objectness scores of both original patches that are mixed in IR. Then, we apply a separate loss suitable for each type in an unsupervised manner. The proposed losses dramatically improve the performance of semi-supervised learning as well as supervised learning. In the supervised learning setting, our method improves the baseline methods by a significant margin. In the semi-supervised learning setting, our algorithm improves the performance on a benchmark dataset (PASCAL VOC and MSCOCO) in a benchmark architecture (SSD).

关键词： Interpolation Supervised learning Object detection Detectors computer architecture Semisupervised learning Benchmark testing

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Depth-attentional Features for Single-image Rain Removal 32

Depth-attentional Features for Single-image Rain Removal

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32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hu, Xiaowei Fu, Chi-Wing Zhu, Lei Heng, Pheng-Ann Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Acad Sci Guangdong Prov Key Lab Comp Vis & Virtual Real Te Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9781728132938

Rain is a common weather phenomenon, where object visibility varies with depthfrom the cameraand objects faraway are visually blocked more by fog than by rain streaks. Existing methods and datasets for rain removal, however ignore these physical properties, thereby limiting the rain removal efficiency on real photos. In this work, we first analyze the visual effects of rain subject to scene depth andformulate a rain imaging model collectively with rain streaks and fog;by then, we prepare a new dataset called RainCityscapes with rain streaks and fog on real outdoor photos. Furthermore,we design an end-to-end deep neural network, where we train it to learn depth-attentionalfeatures via a depth-guidedattentionmechanism, and regress a residualmap to produce the rain-free image output. We performed various experiments to visually and quantitatively compare our method with several state-of-the-art methods to demonstrate its superiorityover the others.

关键词： Computational Photography Deep Learning Low-level vision vision Applications and Systems

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