activity
20142023
most citedBeyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)

152 citations · 1.1k across the 56 of their papers we have counts for

collaborators

56 papers

cs.CV2023★ 3 cited

Identity-Seeking Self-Supervised Representation Learning for Generalizable Person Re-identification

Zhaopeng Dou, Zhongdao Wang, Yali Li +1

This paper aims to learn a domain-generalizable (DG) person re-identification (ReID) representation from large-scale videos \textbf{without any annotation}. Prior DG ReID methods e…

cs.RO2023★ 1 cited

VL-Grasp: a 6-Dof Interactive Grasp Policy for Language-Oriented Objects in Cluttered Indoor Scenes

Yuhao Lu, Yixuan Fan, Beixing Deng +3

Robotic grasping faces new challenges in human-robot-interaction scenarios. We consider the task that the robot grasps a target object designated by human's language directives. Th…

cs.CV2023★ 2 cited

Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection

Yujiang Pu, Xiaoyu Wu, Lulu Yang +1

Video anomaly detection under weak supervision presents significant challenges, particularly due to the lack of frame-level annotations during training. While prior research has ut…

physics.optics2023★ 1 cited

Spectral Convolutional Neural Network Chip for In-sensor Edge Computing of Incoherent Natural Light

Kaiyu Cui, Shijie Rao, Sheng Xu +9

Convolutional neural networks (CNNs) are representative models of artificial neural networks (ANNs). However, the considerable power consumption and limited computing speed of elec…

cs.CV2023★ 4 cited

Detecting Everything in the Open World: Towards Universal Object Detection

Zhenyu Wang, Yali Li, Xi Chen +4

In this paper, we formally address universal object detection, which aims to detect every scene and predict every category. The dependence on human annotations, the limited visual…

cs.CV2022★ 2 cited

Generalizable Re-Identification from Videos with Cycle Association

Zhongdao Wang, Zhaopeng Dou, Jingwei Zhang +4

In this paper, we are interested in learning a generalizable person re-identification (re-ID) representation from unlabeled videos. Compared with 1) the popular unsupervised re-ID…