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

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On Learning Contrastive Representations for Learning with Noisy Labels

On Learning Contrastive Representations for Learning with No...

引用

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

作者： Yi, Li Liu, Sheng She, Qi McLeod, A. Ian Wang, Boyu Univ Western Ontario London ON Canada NYU Ctr Data Sci New York NY 10003 USA ByteDance Inc Beijing Peoples R China Vector Inst Toronto ON Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep neural networks are able to memorize noisy labels easily with a softmax cross entropy (CE) loss. Previous studies attempted to address this issue focus on incorporating a noise-robust loss function to the CE loss. However, the memorization issue is alleviated but still remains due to the non-robust CE loss. To address this issue, we focus on learning robust contrastive representations of data on which the classifier is hard to memorize the label noise under the CE loss. We propose a novel contrastive regularization function to learn such representations over noisy data where label noise does not dominate the representation learning. By theoretically investigating the representations induced by the proposed regularization function, we reveal that the learned representations keep information related to true labels and discard information related to corrupted labels. Moreover, our theoretical results also indicate that the learned representations are robust to the label noise. The effectiveness of this method is demonstrated with experiments on benchmark datasets.

关键词： Representation learning Deep learning Ethics computer vision Neural networks Entropy Noise robustness

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EVSRNet: Efficient Video Super-Resolution with Neural Architecture Search

EVSRNet: Efficient Video Super-Resolution with Neural Archit...

引用

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

作者： Liu, Shaoli Zheng, Chengjian Lu, Kaidi Gao, Si Wang, Ning Wang, Bofei Zhang, Diankai Zhang, Xiaofeng Xu, Tianyu ZTE Corp State Key Lab Mobile Network & Mobile Multimedia Shenzhen Peoples R China

ISBN: (纸本)9781665448994

With the development of convolutional neural networks (CNN), the super-resolution results of CNN-based method have far surpassed traditional method. In particular, the CNN-based single image super-resolution method has achieved excellent results. Video sequences contain more abundant information compare with image, but there are few video super-resolution methods that can be applied to mobile devices due to the requirement of heavy computation, which limits the application of video super-resolution. In this work, we propose the Efficient Video Super-Resolution Network (EVSRNet) with neural architecture search for real-time video super-resolution. Extensive experiments show that our method achieves a good balance between quality and efficiency. Finally, we achieve a competitive result of 7.36 where the PSNR is 27.85 dB and the inference time is 11.3 ms/f on the target snapdragon 865 SoC, resulting in a 2nd place in the Mobile AI(MAI) 2021 real-time video super-resolution challenge. It is noteworthy that, our method is the fastest and significantly outperforms other competitors by large margins.

关键词： Visualization Superresolution Video sequences Neural networks computer architecture Streaming media Real-time systems

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Object-aware Video-language Pre-training for Retrieval

Object-aware Video-language Pre-training for Retrieval

引用

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

作者： Wang, Alex Jinpeng Ge, Yixiao Cai, Guanyu Yan, Rui Lin, Xudong Shan, Ying Qie, Xiaohu Shou, Mike Zheng Natl Univ Singapore Show Lab Singapore Singapore ARC Lab Beijing Peoples R China Tencent PCG Beijing Peoples R China Columbia Univ New York NY 10027 USA Tongji Univ Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, by introducing large-scale dataset and strong transformer network, video-language pre-training has shown great success especially for retrieval. Yet, existing video-language transformer models do not explicitly fine-grained semantic align. In this work, we present Object-aware Transformers, an object-centric approach that extends video-language transformer to incorporate object representations. The key idea is to leverage the bounding boxes and object tags to guide the training process. We evaluate our model on three standard sub-tasks of video-text matching on four widely used benchmarks. We also provide deep analysis and detailed ablation about the proposed method. We show clear improvement in performance across all tasks and datasets considered, demonstrating the value of a model that incorporates object representations into a video-language architecture. The code has been released in https://***/FingerRec/OA-Transformer.

关键词： Training Visualization Machine vision Semantics computer architecture Detectors Transformers

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Imposing Consistency for Optical Flow Estimation

Imposing Consistency for Optical Flow Estimation

引用

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

作者： Jeong, Jisoo Lin, Jamie Menjay Porikli, Fatih Kwak, Nojun Qualcomm AI Res San Diego CA 92121 USA Google Res Mountain View CA USA Seoul Natl Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Imposing consistency through proxy tasks has been shown to enhance data-driven learning and enable self-supervision in various tasks. This paper introduces novel and effective consistency strategies for optical flow estimation, a problem where labels from real-world data are very challenging to derive. More specifically, we propose occlusion consistency and zero forcing in the forms of self-supervised learning and transformation consistency in the form of semi-supervised learning. We apply these consistency techniques in a way that the network model learns to describe pixel-level motions better while requiring no additional annotations. We demonstrate that our consistency strategies applied to a strong baseline network model using the original datasets and labels provide further improvements, attaining the state-of-the-art results on the KITTI-2015 scene flow benchmark in the non-stereo category. Our method achieves the best foreground accuracy (4.33% in Fl-all) over both the stereo and non-stereo categories, even though using only monocular image inputs.

关键词： computer vision Tracking Annotations Estimation Self-supervised learning Semisupervised learning Benchmark testing

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Transferable Query Selection for Active Domain Adaptation

Transferable Query Selection for Active Domain Adaptation

引用

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

作者： Fu, Bo Cao, Zhangjie Wang, Jianmin Long, Mingsheng Tsinghua Univ Sch Software BNRist Beijing Peoples R China

ISBN: (纸本)9781665445092

Unsupervised domain adaptation (UDA) enables transferring knowledge from a related source domain to a fully unlabeled target domain. Despite the significant advances in UDA, the performance gap remains quite large between UDA and supervised learning with fully labeled target data. Active domain adaptation (ADA) mitigates the gap under minimal annotation cost by selecting a small quota of target samples to annotate and incorporating them into training. Due to the domain shift, the query selection criteria of prior active learning methods may be ineffective to select the most informative target samples for annotation. In this paper, we propose Transferable Query Selection (TQS), which selects the most informative samples under domain shift by an ensemble of three new criteria: transferable committee, transferable uncertainty, and transferable domainness. We further develop a randomized selection algorithm to enhance the diversity of the selected samples. Experiments show that TQS remarkably outperforms previous UDA and ADA methods on several domain adaptation datasets. Deeper analyses demonstrate that TQS can select the most informative target samples under the domain shift.

关键词： Training computer vision Uncertainty Costs Annotations Supervised learning Benchmark testing

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Multi stain graph fusion for multimodal integration in pathology

Multi stain graph fusion for multimodal integration in patho...

引用

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

作者： Dwivedi, Chaitanya Nofallah, Shima Pouryahya, Maryam Iyer, Janani Leidal, Kenneth Chung, Chuhan Watkins, Timothy Billin, Andrew Myers, Robert Abel, John Behrooz, Ali PathAI Inc Boston MA 02215 USA Gilead Sci Inc Foster City CA 94404 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary information from multiple non-registered histopathology images to predict pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis (NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary assessment of NASH typically requires liver biopsy evaluation on two histological stains: Trichrome (TC) and hematoxylin and eosin (H&E). Our multimodal approach learns to extract complementary information from TC and H&E graphs corresponding to each stain while simultaneously learning an optimal policy to combine this information. We report up to 20% improvement in predicting fibrosis stage and NAS component grades over single-stain modeling approaches, measured by computing linearly weighted Cohen's kappa between machinederived vs. pathologist consensus scores. Broadly, this paper demonstrates the value of leveraging diverse pathology images for improved ML-powered histologic assessment.

关键词： Weight measurement computer vision Histopathology Computational modeling conferences Liver Predictive models

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Multi-class Token Transformer for Weakly Supervised Semantic Segmentation

Multi-class Token Transformer for Weakly Supervised Semantic...

引用

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

作者： Xu, Lian Ouyang, Wanli Bennamoun, Mohammed Boussaid, Farid Xu, Dan Univ Western Australia Nedlands WA Australia Univ Sydney SenseTime Comp Vis Grp Sydney NSW Australia Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper proposes a new transformer-based framework to learn class-specific object localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS). Inspired by the fact that the attended regions of the one-class token in the standard vision transformer can be leveraged to form a class-agnostic localization map, we investigate if the transformer model can also effectively capture class-specific attention for more discriminative object localization by learning multiple class tokens within the transformer. To this end, we propose a Multi-class Token Transformer, termed as MCTformer, which uses multiple class tokens to learn interactions between the class tokens and the patch tokens. The proposed MCTformer can successfully produce class-discriminative object localization maps from the class-to-patch attentions corresponding to different class tokens. We also propose to use a patchlevel pairwise affinity, which is extracted from the patchto-patch transformer attention, to further refine the localization maps. Moreover, the proposed framework is shown to fully complement the Class Activation Mapping (CAM) method, leading to remarkably superior WSSS results on the PASCAL VOC and MS COCO datasets. These results underline the importance of the class token for WSSS.(-1)

关键词： Location awareness computer vision Shape Semantics Object detection Transformers pattern recognition

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Compatibility-aware Heterogeneous Visual Search

Compatibility-aware Heterogeneous Visual Search

引用

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

作者： Duggal, Rahul Zhou, Hao Yang, Shuo Xiong, Yuanjun Xia, Wei Tu, Zhuowen Soatto, Stefano Amazon AI AWS Seattle WA 98121 USA Georgia Inst Technol Atlanta GA 30332 USA Amazon AI Seattle WA USA

ISBN: (纸本)9781665445092

We tackle the problem of visual search under resource constraints. Existing systems use the same embedding model to compute representations (embeddings) for the query and gallery images. Such systems inherently face a hard accuracy-efficiency trade-off: the embedding model needs to be large enough to ensure high accuracy, yet small enough to enable query-embedding computation on resource-constrained platforms. This trade-off could be mitigated if gallery embeddings are generated from a large model and query embeddings are extracted using a compact model. The key to building such a system is to ensure representation compatibility between the query and gallery models. In this paper, we address two forms of compatibility: One enforced by modifying the parameters of each model that computes the embeddings. The other by modifying the architectures that compute the embeddings, leading to compatibility-aware neural architecture search (CMP-NAS). We test CMP-NAS on challenging retrieval tasks for fashion images (DeepFashion2), and face images (IJB-C). Compared to ordinary (homogeneous) visual search using the largest embedding model (paragon), CMP-NAS achieves 80-fold and 23-fold cost reduction while maintaining accuracy within 0:3% and 1:6% of the paragon on DeepFashion2 and IJB-C respectively.

关键词： Visualization computer vision Costs Computational modeling Face recognition Architecture Buildings

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Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation

Overcoming Catastrophic Forgetting in Incremental Object Det...

引用

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

作者： Feng, Tao Wang, Mang Yuan, Hangjie Alibaba Grp Hangzhou Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Traditional object detectors are ill-equipped for incremental learning. However, fine-tuning directly on a well-trained detection model with only new data will lead to catastrophic forgetting. Knowledge distillation is a flexible way to mitigate catastrophic forgetting. In Incremental Object Detection (IOD), previous work mainly focuses on distilling for the combination of features and responses. However, they under-explore the information that contains in responses. In this paper, we propose a response-based incremental distillation method, dubbed Elastic Response Distillation (ERD), which focuses on elastically learning responses from the classification head and the regression head. Firstly, our method transfers category knowledge while equipping student detector with the ability to retain localization information during incremental learning. In addition, we further evaluate the quality of all locations and provide valuable responses by the Elastic Response Selection (ERS) strategy. Finally, we elucidate that the knowledge from different responses should be assigned with different importance during incremental distillation. Extensive experiments conducted on MS COCO demonstrate our method achieves state-of-the-art result, which substantially narrows the performance gap towards full training.

关键词： Location awareness Training computer vision Codes Object detection Detectors Feature extraction

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VMCML: Video and Music Matching via Cross-Modality Lifting

VMCML: Video and Music Matching via Cross-Modality Lifting

引用

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

作者： Lee, Yi-Shan Tseng, Wei-Cheng Wang, Fu-En Sun, Min Natl Tsing Hua Univ Hsinchu Taiwan Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9798350365474

We propose a content-based system for matching video and background music. The system aims to address the challenges in music recommendation for new users or new music give short-form videos. To this end, we propose a cross-modal framework VMCML (Video and Music Matching via Cross-Modality Lifting) that finds a shared embedding space between video and music representations. To ensure the embedding space can be effectively shared by both representations, we leverage CosFace loss based on margin-based cosine similarity loss. Furthermore, to confirm the music is not the original sound of the video and that more than one video is matched to the same music, we follow the rule and collect videos and music from a well-known multi-media platform. That is because there are limitations of previous datasets. We establish a large-scale dataset called MSV, which provide 390 individual music and the corresponding matched 150,000 videos. We conduct extensive experiments on Youtube-8M and our MSV datasets. Our quantitative and qualitative results demonstrate the effectiveness of our proposed framework and achieve state-of-the-art video and music matching performance.

关键词： computer music

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