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531 results on '"Zhao, Rui"'

28. UniHCP: A Unified Model for Human-Centric Perceptions

Author

Ci, Yuanzheng, primary, Wang, Yizhou, additional, Chen, Meilin, additional, Tang, Shixiang, additional, Bai, Lei, additional, Zhu, Feng, additional, Zhao, Rui, additional, Yu, Fengwei, additional, Qi, Donglian, additional, and Ouyang, Wanli, additional

Published
2023
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29. Robust Partial Fingerprint Recognition

Author

Zhang, Yufei, primary, Zhao, Rui, additional, Zhao, Ziyi, additional, Ramakrishnan, Naveen, additional, Aggarwal, Manoj, additional, Medioni, Gerard, additional, and Ji, Qiang, additional

Published
2023
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33. Spatial-Temporal Graphs Plus Transformers for Geometry-Guided Facial Expression Recognition.

Author

Zhao, Rui, Liu, Tianshan, Huang, Zixun, Lun, Daniel P.K., and Lam, Kin-Man

Abstract

Facial expression recognition (FER) is of great interest to the current studies of human-computer interaction. In this paper, we propose a novel geometry-guided facial expression recognition framework, based on graph convolutional networks and transformers, to perform effective emotion recognition from videos. Specifically, we detect and utilize facial landmarks to construct a spatial-temporal graph, based on both the landmark coordinates and local appearance, for representing a facial expression sequence. The graph convolutional blocks and transformer modules are employed to produce high-semantic emotion-related representations from the structured facial graphs, which facilitate the framework to establish both the local and non-local dependency between the vertices. Moreover, spatial and temporal attention mechanisms are introduced into graph-based learning to promote FER reasoning, via the emphasis on the most informative facial components and frames. Extensive experiments demonstrate that the proposed framework achieves promising performance for geometry-based FER and shows great generalization and robustness in real-world applications. [ABSTRACT FROM AUTHOR]

Published
2023
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46. Feature Erasing and Diffusion Network for Occluded Person Re-Identification

Author

Wang, Zhikang, Zhu, Feng, Tang, Shixiang, Zhao, Rui, He, Lihuo, and Song, Jiangning

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

Occluded person re-identification (ReID) aims at matching occluded person images to holistic ones across different camera views. Target Pedestrians (TP) are usually disturbed by Non-Pedestrian Occlusions (NPO) and NonTarget Pedestrians (NTP). Previous methods mainly focus on increasing model's robustness against NPO while ignoring feature contamination from NTP. In this paper, we propose a novel Feature Erasing and Diffusion Network (FED) to simultaneously handle NPO and NTP. Specifically, NPO features are eliminated by our proposed Occlusion Erasing Module (OEM), aided by the NPO augmentation strategy which simulates NPO on holistic pedestrian images and generates precise occlusion masks. Subsequently, we Subsequently, we diffuse the pedestrian representations with other memorized features to synthesize NTP characteristics in the feature space which is achieved by a novel Feature Diffusion Module (FDM) through a learnable cross attention mechanism. With the guidance of the occlusion scores from OEM, the feature diffusion process is mainly conducted on visible body parts, which guarantees the quality of the synthesized NTP characteristics. By jointly optimizing OEM and FDM in our proposed FED network, we can greatly improve the model's perception ability towards TP and alleviate the influence of NPO and NTP. Furthermore, the proposed FDM only works as an auxiliary module for training and will be discarded in the inference phase, thus introducing little inference computational overhead. Experiments on occluded and holistic person ReID benchmarks demonstrate the superiority of FED over state-of-the-arts, where FED achieves 86.3% Rank-1 accuracy on Occluded-REID, surpassing others by at least 4.7%., Comment: 10 pages, 5 figures

Published
2022

47. UniVIP: A Unified Framework for Self-Supervised Visual Pre-training

Author

Li, Zhaowen, Zhu, Yousong, Yang, Fan, Li, Wei, Zhao, Chaoyang, Chen, Yingying, Chen, Zhiyang, Xie, Jiahao, Wu, Liwei, Zhao, Rui, Tang, Ming, and Wang, Jinqiao

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centric-object images like those in ImageNet and ignores the correlation among the scene and instances, as well as the semantic difference of instances in the scene. To address the above problems, we propose a Unified Self-supervised Visual Pre-training (UniVIP), a novel self-supervised framework to learn versatile visual representations on either single-centric-object or non-iconic dataset. The framework takes into account the representation learning at three levels: 1) the similarity of scene-scene, 2) the correlation of scene-instance, 3) the discrimination of instance-instance. During the learning, we adopt the optimal transport algorithm to automatically measure the discrimination of instances. Massive experiments show that UniVIP pre-trained on non-iconic COCO achieves state-of-the-art transfer performance on a variety of downstream tasks, such as image classification, semi-supervised learning, object detection and segmentation. Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2.5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential., Comment: Accepted by CVPR2022

Published
2022

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