activity
20192023
most citedAsymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification

13 citations · 44 across the 13 of their papers we have counts for

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11 papers · 1 filter

cs.CV20231 cited

RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias

Hao Cheng, Jinhao Duan, Hui Li +6

Recently, there has been a surge of interest and attention in Transformer-based structures, such as Vision Transformer (ViT) and Vision Multilayer Perceptron (VMLP). Compared with…

cs.CV2023

An End-to-End Framework of Road User Detection, Tracking, and Prediction from Monocular Images

Hao Cheng, Mengmeng Liu, Lin Chen

Perception that involves multi-object detection and tracking, and trajectory prediction are two major tasks of autonomous driving. However, they are currently mostly studied separa…

cs.CV20219 cited

RMNet: Equivalently Removing Residual Connection from Networks

Fanxu Meng, Hao Cheng, Jiaxin Zhuang +2

Although residual connection enables training very deep neural networks, it is not friendly for online inference due to its multi-branch topology. This encourages many researchers…

cs.CV2021

Disentangled Feature Representation for Few-shot Image Classification

Hao Cheng, Yufei Wang, Haoliang Li +2

Learning the generalizable feature representation is critical for few-shot image classification. While recent works exploited task-specific feature embedding using meta-tasks for f…

cs.CV20202 cited

One for More: Selecting Generalizable Samples for Generalizable ReID Model

Enwei Zhang, Xinyang Jiang, Hao Cheng +7

Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the p…

cs.CV2020

Exploring Dynamic Context for Multi-path Trajectory Prediction

Hao Cheng, Wentong Liao, Xuejiao Tang +3

To accurately predict future positions of different agents in traffic scenarios is crucial for safely deploying intelligent autonomous systems in the real-world environment. Howeve…