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检索条件"机构=Shanghai Key Lab. of Digital Media Processing and Transmission"
45 条 记 录,以下是1-10 订阅
排序:
M-RAT: a Multi-grained Retrieval Augmentation Transformer for Image Captioning  17th
M-RAT: a Multi-grained Retrieval Augmentation Transformer f...
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17th Asian Conference on Computer Vision, ACCV 2024
作者: Song, Jiayan Pan, Renjie Zhou, Jun Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai200240 China
Current encoder-decoder methods for image captioning mai-nly consist of an object detection module (two-stage), or rely on big models with large-scale datasets to improve the effectiveness, which leads to increasing c... 详细信息
来源: 评论
Hydrodynamics-Informed Neural Network for Simulating Dense Crowd Motion Patterns  24
Hydrodynamics-Informed Neural Network for Simulating Dense C...
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32nd ACM International Conference on Multimedia, MM 2024
作者: Zhou, Yanshan Lai, Pingrui Yu, Jiaqi Xiong, Yingjie Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China
With global occurrences of crowd crushes and stampedes, dense crowd simulation has been drawing great attention. In this research, our goal is to simulate dense crowd motions under six classic motion patterns, more sp... 详细信息
来源: 评论
Sine: Similarity-Regularized Intra-Class Exploitation for Cross-Granularity Few-Shot Learning  48
Sine: Similarity-Regularized Intra-Class Exploitation for Cr...
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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023
作者: Yang, Jinhai Yang, Hua Shanghai Jiao Tong University Institution of Image Communication and Network Engineering China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China
Few-shot learning aims for rapid adaptation with few samples. Recently, cross-granularity few-shot learning has emerged as a promising research area, where models observe coarse lab.ls but target fine-grained recognit... 详细信息
来源: 评论
Adaptive and Collab.rative Multi-scale Alignment for Text-Based Person Search
Adaptive and Collaborative Multi-scale Alignment for Text-Ba...
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2023 IEEE International Conference on Visual Communications and Image processing, VCIP 2023
作者: Yang, Xinxin Pan, Renjie Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute China
Text-To-image person search is challenging due to the cross-scale correspondences and information inequality between modalities. Specifically, images and text are complexly linked at different scales and images are us... 详细信息
来源: 评论
FC-GNN: Recovering Reliable and Accurate Correspondences from Interferences
FC-GNN: Recovering Reliable and Accurate Correspondences fro...
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Conference on Computer Vision and Pattern Recognition (CVPR)
作者: Haobo Xu Jun Zhou Hua Yang Renjie Pan Cunyan Li Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai Key Lab of Digital Media Processing and Transmission
Finding correspondences between images is essential for many computer vision tasks and sparse matching pipelines have been popular for decades. However, matching noise within and between images, along with inconsisten... 详细信息
来源: 评论
Physics-Environment Interaction Network for Dense Crowd Behavior Recognition
SSRN
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SSRN 2024年
作者: Yu, Jiaqi Zhou, Yanshan Pan, Renjie Lai, Pingrui Yang, Hua Institute of Image Communication and Network Engineering Shanghai Key Lab of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai200240 China
The analysis of large-scale crowd behavior plays a crucial role in public safety. However, intelligent systems face three major challenges in analyzing dense crowd behavior: the severe occlusion between individuals, t... 详细信息
来源: 评论
Background Clustering Pre-Training for Few-Shot Segmentation
Background Clustering Pre-Training for Few-Shot Segmentation
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IEEE International Conference on Image processing
作者: Zhimiao Yu Tiancheng Lin Yi Xu Shanghai Key Lab of Digital Media Processing and Transmission Shanghai Jiao Tong University Chongqing Research Institute of Shanghai Jiao Tong University
Recent few-shot segmentation (FSS) methods introduce an extra pre-training stage before meta-training to obtain a stronger backbone, which has become a standard step in few-shot learning. Despite the effectiveness, cu...
来源: 评论
L2RT-FIQA: Face Image Quality Assessment via Learning-to-Rank Transformer  9th
L2RT-FIQA: Face Image Quality Assessment via Learning-to-Ra...
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9th International Forum on digital Multimedia Communication, IFTC 2022
作者: Chen, Zehao Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China
Face recognition (FR) systems are easily constrained by complex environmental situations in the wild. To ensure the accuracy of FR systems, face image quality assessment (FIQA) is applied to reject low-quality face im... 详细信息
来源: 评论
Sine: Similarity-Regularized Intra-Class Exploitation for Cross-Granularity Few-Shot Learning
Sine: Similarity-Regularized Intra-Class Exploitation for Cr...
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International Conference on Acoustics, Speech, and Signal processing (ICASSP)
作者: Jinhai Yang Hua Yang Institution of Image Communication and Network Engineering Shanghai Jiao Tong University China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China
Few-shot learning aims for rapid adaptation with few samples. Recently, cross-granularity few-shot learning has emerged as a promising research area, where models observe coarse lab.ls but target fine-grained recognit... 详细信息
来源: 评论
Spatial-Temporal Constrained Pseudo-lab.ling for Unsupervised Person Re-identification via GCN Inference  18th
Spatial-Temporal Constrained Pseudo-labeling for Unsupervis...
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18th International Forum of digital Multimedia Communication, IFTC 2021
作者: Ling, Sen Yang, Hua Liu, Chuang Chen, Lin Zhao, Hongtian The Institute of Image Communication and Network Engineering Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China
Most existing unsupervised person re-identification (Re-ID) methods primarily depend on the cluster distance, and merely exploit the availab.e source lab.led data to assign pseudo lab.ls for the unannotated data. Wher... 详细信息
来源: 评论