activity
20172023
most citedMixed High-Order Attention Network for Person Re-Identification

40 citations · 172 across the 13 of their papers we have counts for

collaborators

13 papers

cs.CV2023★ 1 cited

DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving

Jun-Yan He, Zhi-Qi Cheng, Chenyang Li +5

Real-time perception, or streaming perception, is a crucial aspect of autonomous driving that has yet to be thoroughly explored in existing research. To address this gap, we presen…

cs.CV2022★ 1 cited

Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing

Kaicheng Li, Hongyu Yang, Binghui Chen +3

Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack t…

cs.CV2022★ 15 cited

NeRF-In: Free-Form NeRF Inpainting with RGB-D Priors

Hao-Kang Liu, I-Chao Shen, Bing-Yu Chen

Though Neural Radiance Field (NeRF) demonstrates compelling novel view synthesis results, it is still unintuitive to edit a pre-trained NeRF because the neural network's parameters…

cs.CV2022★ 5 cited

Dense Learning based Semi-Supervised Object Detection

Binghui Chen, Pengyu Li, Xiang Chen +3

Semi-supervised object detection (SSOD) aims to facilitate the training and deployment of object detectors with the help of a large amount of unlabeled data. Though various self-tr…

cs.CV2021

Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting

Binghui Chen, Zhaoyi Yan, Ke Li +4

In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity…

cs.CV2019★ 40 cited

Mixed High-Order Attention Network for Person Re-Identification

Binghui Chen, Weihong Deng, Jiani Hu

Attention has become more attractive in person reidentification (ReID) as it is capable of biasing the allocation of available resources towards the most informative parts of an in…