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conference on computer vision and pattern recognition (CVPR)

作者： Leming Guo Wanli Xue Qing Guo Bo Liu Kaihua Zhang Tiantian Yuan Shengyong Chen School of Computer Science and Engineering Tianjin University of Technology Centre for Frontier AI Research (CFAR) A*STAR Singapore Walmart Global Tech Sunnyvale CA USA School of Computer and Software Nanjing University of Information Science and Technology Technical College for the Deaf Tianjin University of Technology

Continuous sign language recognition (CSLR) aims to recognize glosses in a sign language video. State-of-the-art methods typically have two modules, a spatial perception module and a temporal aggregation module, which are jointly learned end-to-end. Existing results in [9, 20, 25, 36] have indicated that, as the frontal component of the over-all model, the spatial perception module used for spatial feature extraction tends to be insufficiently trained. In this paper, we first conduct empirical studies and show that a shallow temporal aggregation module allows more thor-ough training of the spatial perception module. However, a shallow temporal aggregation module cannot well capture both local and global temporal context information in sign language. To address this dilemma, we propose a cross-temporal context aggregation (CTCA) model. Specifically, we build a dual-path network that contains two branches for perceptions of local temporal context and global temporal context. We further design a cross-context knowledge distil-lation learning objective to aggregate the two types of con-text and the linguistic prior. The knowledge distillation en-ables the resultant one-branch temporal aggregation mod-ule to perceive local-global temporal and semantic context. This shallow temporal perception module structure facili-tates spatial perception module learning. Extensive exper-iments on challenging CSLR benchmarks demonstrate that our method outperforms all state-of-the-art methods.

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Correction to: Machine translation from signed to spoken languages: state of the art and challenges (Universal Access in the Information Society, (2023), 10.1007/s10209-023-00992-1)

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Universal Access in the Information Society 2024年第1期24卷 953-957页

作者： De Coster, Mathieu Shterionov, Dimitar Van Herreweghe, Mieke Dambre, Joni IDLab-AIRO Ghent University - imec Technologiepark-Zwijnaarde 126 Ghent9052 Belgium Tilburg University Warandelaan 2 Tilburg5037 AB Netherlands Ghent University Blandijnberg 2 Ghent9000 Belgium

In this article the incorrect Fig. 7 appear as below: (Figure presented.) The Fig. 7 should have appeared as shown below. (Figure presented.) Transformers tend to outperform RNNs on different SLT tasks in terms of BLEU-4 score on the RWTH-PHOENIX-Weather 2014T dataset In this article the incorrect Fig. 8 appear as below: (Figure presented.) The Fig. 8 should have appeared as shown below. (Figure presented.) Evolution of model scores on the RWTH-PHOENIX-Weather 2014T per task In this article the incorrect Table 6 appear as below: (Figure presented.) The Table 6 should have appeared as shown below. (Figure presented.) The Table 7 appears incorrectly as below. (Figure presented.) The correct Table 7 should be shown as below. (Figure presented.) This erratum contains changes to the findings from two of the cited papers. For citations [88] and [99]: [88] Chen, Y., Zuo, R., Wei, F., Wu, Y., Liu, S., & Mak, B.: Two-stream network for sign language recognition and translation. Advances in Neural Information Processing Systems, 35, 17043–17056 (2022) [99] Chen, Y., Wei, F., Sun, X., Wu, Z., Lin, S.: A simple multi-modality transfer learning baseline for sign language translation. In: Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pp. 5120–5130 (2022) The original article has been corrected. © 2024, Springer-Verlag GmbH Germany, part of Springer Nature.

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Sparsely Annotated Semantic Segmentation with Adaptive Gaussian Mixtures

Sparsely Annotated Semantic Segmentation with Adaptive Gauss...

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conference on computer vision and pattern recognition (CVPR)

作者： Linshan Wu Zhun Zhong Leyuan Fang Xingxin He Qiang Liu Jiayi Ma Hao Chen College of Electrical and Information Engineering Hunan University Department of Information Engineering and Computer Science University of Trento School of Electronic Information Wuhan University Department of Computer Science and Engineering Hong Kong University of Science and Technology

Sparsely annotated semantic segmentation (SASS) aims to learn a segmentation model by images with sparse labels (i.e., points or scribbles). Existing methods mainly focus on introducing low-level affinity or generating pseudo labels to strengthen supervision, while largely ignoring the inherent relation between labeled and unlabeled pixels. In this paper, we observe that pixels that are close to each other in the feature space are more likely to share the same class. Inspired by this, we propose a novel SASS framework, which is equipped with an Adaptive Gaussian Mixture Model (AGMM). Our AGMM can effectively endow reliable supervision for unlabeled pixels based on the distributions of labeled and unlabeled pixels. Specifically, we first build Gaussian mixtures using labeled pixels and their relatively similar unlabeled pixels, where the labeled pixels act as centroids, for modeling the feature distribution of each class. Then, we leverage the reliable information from labeled pixels and adaptively generated GMM predictions to supervise the training of unlabeled pixels, achieving online, dynamic, and robust selfsupervision. In addition, by capturing category-wise Gaussian mixtures, AGMM encourages the model to learn discriminative class decision boundaries in an end-to-end contrastive learning manner. Experimental results conducted on the PASCAL VOC 2012 and Cityscapes datasets demonstrate that our AGMM can establish new state-of-the-art SASS performance. Code is available at https://***/Luffy03/AGMM-SASS

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Are We Ready for vision-Centric Driving Streaming Perception? The ASAP Benchmark

Are We Ready for Vision-Centric Driving Streaming Perception...

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conference on computer vision and pattern recognition (CVPR)

作者： Xiaofeng Wang Zheng Zhu Yunpeng Zhang Guan Huang Yun Ye Wenbo Xu Ziwei Chen Xingang Wang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences PhiGent Robotics Southeast University

In recent years, vision-centric perception has flourished in various autonomous driving tasks, including 3D detection, semantic map construction, motion forecasting, and depth estimation. Nevertheless, the latency of vision-centric approaches is too high for practical deployment (e.g., most camera-based 3D detectors have a runtime greater than 300ms). To bridge the gap between ideal researches and real-world applications, it is necessary to quantify the trade-off between performance and efficiency. Traditionally, autonomous-driving perception benchmarks perform the offline evaluation, neglecting the inference time delay. To mitigate the problem, we propose the Autonomous-driving StreAming Perception (ASAP) benchmark, which is the first benchmark to evaluate the online performance of vision-centric perception in autonomous driving. On the basis of the 2Hz annotated nuScenes dataset, we first propose an annotation-extending pipeline to generate high-frame-rate labels for the 12Hz raw images. Referring to the practical deployment, the Streaming Perception Under constRained-computation (SPUR) evaluation protocol is further constructed, where the 12Hz inputs are utilized for streaming evaluation under the constraints of different computational resources. In the ASAP benchmark, comprehensive experiment results reveal that the model rank alters under different constraints, suggesting that the model latency and computation budget should be considered as design choices to optimize the practical deployment. To facilitate further research, we establish baselines for camera-based streaming 3D detection, which consistently enhance the streaming performance across various hardware. ASAP project page: https://***/JeffWang987/ASAP.

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ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos

ANetQA: A Large-scale Benchmark for Fine-grained Composition...

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conference on computer vision and pattern recognition (CVPR)

作者： Zhou Yu Lixiang Zheng Zhou Zhao Fei Wu Jianping Fan Kui Ren Jun Yu School of Computer Science Hangzhou Dianzi University China Colledge of Computer Science and Technology Zhejiang University China AI Lab at Lenovo Research China School of Cyber Science and Technology Zhejiang University China

Building benchmarks to systemically analyze different capabilities of video question answering (VideoQA) models is challenging yet crucial. Existing benchmarks often use non-compositional simple questions and suffer from language biases, making it difficult to diagnose model weaknesses incisively. A recent benchmark AGQA [8] poses a promising paradigm to generate QA pairs automatically from pre-annotated scene graphs, enabling it to measure diverse reasoning abilities with granular control. However, its questions have limitations in reasoning about the fine-grained semantics in videos as such information is absent in its scene graphs. To this end, we present ANetQA, a large-scale benchmark that supports fine-grained compositional reasoning over the challenging untrimmed videos from ActivityNet [4]. Similar to AGQA, the QA pairs in ANetQA are automatically generated from annotated video scene graphs. The fine-grained properties of ANetQA are reflected in the following: (i) untrimmed videos with fine-grained semantics; (ii) spatio-temporal scene graphs with fine-grained taxonomies; and (iii) diverse questions generated from fine-grained templates. ANetQA attains 1.4 billion unbalanced and 13.4 million balanced QA pairs, which is an order of magnitude larger than AGQA with a similar number of videos. Comprehensive experiments are performed for state-of-the-art methods. The best model achieves 44.5% accuracy while human performance tops out at 84.5%, leaving sufficient room for improvement.

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Hierarchical B-Frame Video Coding Using Two-Layer CANF Without Motion Coding

Hierarchical B-Frame Video Coding Using Two-Layer CANF Witho...

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conference on computer vision and pattern recognition (CVPR)

作者： David Alexandre Hsueh-Ming Hang Wen-Hsiao Peng Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Typical video compression systems consist of two main modules: motion coding and residual coding. This general architecture is adopted by classical coding schemes (such as international standards H.265 and H.266) and deep learning-based coding schemes. We propose a novel B-frame coding architecture based on two-layer Conditional Augmented Normalization Flows (CANF). It has the striking feature of not transmitting any motion information. Our proposed idea of video compression without motion coding offers a new direction for learned video coding. Our base layer is a low-resolution image compressor that replaces the full-resolution motion compressor. The low-resolution coded image is merged with the warped high-resolution images to generate a high-quality image as a conditioning signal for the enhancement-layer image coding in full resolution. One advantage of this architecture is significantly reduced computational complexity due to eliminating the motion information compressor. In addition, we adopt a skip-mode coding technique to reduce the transmitted latent samples. The rate-distortion performance of our scheme is slightly lower than that of the state-of-the-art learned B-frame coding scheme, B-CANF, but outperforms other learned B-frame coding schemes. However, compared to B-CANF, our scheme saves 45% of multiply-accumulate operations (MACs) for encoding and 27% of MACs for decoding. The code is available at https://***.

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Semi-Supervised Stereo-Based 3D Object Detection via Cross-View Consensus

Semi-Supervised Stereo-Based 3D Object Detection via Cross-V...

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conference on computer vision and pattern recognition (CVPR)

作者： Wenhao Wu Hau-San Wong Si Wu Department of Computer Science City University of Hong Kong School of Computer Science and Engineering South China University of Technology

Stereo-based 3D object detection, which aims at detecting 3D objects with stereo cameras, shows great potential in low-cost deployment compared to LiDAR-based methods and excellent performance compared to monocular-based algorithms. However, the impressive performance of stereo-based 3D object detection is at the huge cost of high-quality manual annotations, which are hardly attainable for any given scene. Semi-supervised learning, in which limited annotated data and numerous unannotated data are required to achieve a satisfactory model, is a promising method to address the problem of data deficiency. In this work, we propose to achieve semi-supervised learning for stereo-based 3D object detection through pseudo annotation generation from a temporal-aggregated teacher model, which temporally accumulates knowledge from a student model. To facilitate a more stable and accurate depth estimation, we introduce Temporal-Aggregation-Guided (TAG) disparity consistency, a cross-view disparity consistency constraint between the teacher model and the student model for robust and improved depth estimation. To mitigate noise in pseudo annotation generation, we propose a cross-view agreement strategy, in which pseudo annotations should attain high degree of agreements between 3D and 2D views, as well as between binocular views. We perform extensive experiments on the KITTI 3D dataset to demonstrate our proposed method's capability in leveraging a huge amount of unannotated stereo images to attain significantly improved detection results.

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DropKey for vision Transformer

DropKey for Vision Transformer

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conference on computer vision and pattern recognition (CVPR)

作者： Bonan Li Yinhan Hu Xuecheng Nie Congying Han Xiangjian Jiang Tiande Guo Luoqi Liu University of Chinese Academy of Sciences MT Lab Meitu Inc. University of Cambridge

In this paper, we focus on analyzing and improving the dropout technique for self-attention layers of vision Transformer, which is important while surprisingly ignored by prior works. In particular, we conduct researches on three core questions: First, what to drop in self-attention layers? Different from dropping attention weights in literature, we propose to move dropout operations forward ahead of attention matrix calculation and set the Key as the dropout unit, yielding a novel dropout-before-softmax scheme. We theoretically verify that this scheme helps keep both regularization and probability features of attention weights, alleviating the overfittings problem to specific patterns and enhancing the model to globally capture vital information; Second, how to schedule the drop ratio in consecutive layers? In contrast to exploit a constant drop ratio for all layers, we present a new decreasing schedule that gradually decreases the drop ratio along the stack of self-attention layers. We experimentally validate the proposed schedule can avoid overfittings in low-level features and missing in high-level semantics, thus improving the robustness and stableness of model training; Third, whether need to perform structured dropout operation as CNN? We attempt patch-based block-version of dropout operation and find that this useful trick for CNN is not essential for ViT. Given exploration on the above three questions, we present the novel Drop-Key method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way. Comprehensive experiments demonstrate the effectiveness of DropKey for various ViT architectures, e.g. T2T, VOLO, CeiT and DeiT, as well as for various vision tasks, e.g., image classification, object detection, human-object interaction detection and human body shape recovery.

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MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

MIANet: Aggregating Unbiased Instance and General Informatio...

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conference on computer vision and pattern recognition (CVPR)

作者： Yong Yang Qiong Chen Yuan Feng Tianlin Huang School of Computer Science and Engineering South China University of Technology Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application

Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the support set. To address the problem, we propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Specifically, in MIANet, a general information module (GIM) is proposed to extract a general class prototype from word embeddings as a supplement to instance information. To this end, we design a triplet loss that treats the general class prototype as an anchor and samples positive-negative pairs from local features in the support set. The calculated triplet loss can transfer semantic similarities among language identities from a word embedding space to a visual representation space. To alleviate the model biasing towards the seen training classes and to obtain multi-scale information, we then introduce a non-parametric hierarchical prior module (HPM) to generate unbiased instance-level information via calculating the pixel-level similarity between the support and query image features. Finally, an information fusion module (IFM) combines the general and instance information to make predictions for the query image. Extensive experiments on PASCAL-5 i and COCO-20 i show that MIANet yields superior performance and set a new state-of-the-art. Code is available at ***/Aldrich2y/MIANet.

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NTIRE 2024 Challenge on HR Depth from Images of Specular and Transparent Surfaces

NTIRE 2024 Challenge on HR Depth from Images of Specular and...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Pierluigi Zama Ramirez Fabio Tosi Luigi Di Stefano Radu Timofte Alex Costanzino Matteo Poggi Samuele Salti Stefano Mattoccia Yangyang Zhang Cailin Wu Zhuangda He Shuangshuang Yin Jiaxu Dong Yangchenxu Liu Hao Jiang Jun Shi Yong A Yixiang Jin Dingzhe Li Bingxin Ke Anton Obukhov Tinafu Wang Nando Metzger Shengyu Huang Konrad Schindler Yachuan Huang Jiaqi Li Junrui Zhang Yiran Wang Zihao Huang Tianqi Liu Zhiguo Cao Pengzhi Li Jui-Lin Wang Wenjie Zhu Hui Geng Yuxin Zhang Long Lan Kele Xu Tao Sun Qisheng Xu Sourav Saini Aashray Gupta Sahaj K. Mistry Aryan Shukla Vinit Jakhetiya Sunil Jaiswal Yuejin Sun Zhuofan Zheng Yi Ning Jen-Hao Cheng Hou-I Liu Hsiang-Wei Huang Cheng-Yen Yang Zhongyu Jiang Yi-Hao Peng Aishi Huang Jenq-Neng Hwang

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement (NTIRE) workshop at CVPR 2024. This challenge aims to advance the research on depth estimation, specifically to address two of the main open issues in the field: high-resolution and non-Lambertian surfaces. The challenge proposes two tracks on stereo and single-image depth estimation, attracting about 120 registered participants. In the final testing stage, 2 and 8 participating teams submitted their models and fact sheets for the two tracks.

关键词： computer vision conferences Computational modeling Estimation Market research pattern recognition Image restoration

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