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- University of Science and Technology of ChinaCN178 papers
- Chinese Academy of SciencesCN47 papers
- Anhui UniversityCN46 papers
- National University of SingaporeSG36 papers
- Hefei National Center for Physical Sciences at NanoscaleCN29 papers
- Tsinghua UniversityCN24 papers
- Peking UniversityCN20 papers
- Zhejiang UniversityCN17 papers
- Eindhoven University of TechnologyNL16 papers
- Harbin Institute of TechnologyCN14 papers
- Soochow UniversityCN14 papers
- University of Chinese Academy of SciencesCN12 papers
7 papers · 2 filters
3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification
Lin Wu, Yang Wang, Ling Shao +1
In this paper, we introduce a global video representation to video-based person re-identification (re-ID) that aggregates local 3D features across the entire video extent. Most of…
Object Detection with Deep Learning: A Review
Zhong-Qiu Zhao, Peng Zheng, Shou-tao Xu +1
Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection met…
Movie Question Answering: Remembering the Textual Cues for Layered Visual Contents
Bo Wang, Youjiang Xu, Yahong Han +1
Movies provide us with a mass of visual content as well as attracting stories. Existing methods have illustrated that understanding movie stories through only visual content is sti…
IGCV: Interleaved Structured Sparse Convolutional Neural Networks
Guotian Xie, Jingdong Wang, Ting Zhang +3
In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels. In add…
End-to-End Detection and Re-identification Integrated Net for Person Search
Zhenwei He, Lei Zhang, Wei Jia
This paper proposes a pedestrian detection and re-identification (re-id) integration net (I-Net) in an end-to-end learning framework. The I-Net is used in real-world video surveill…
Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval
Lin Wu, Yang Wang, Ling Shao
In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency…