activity
20172020
most citedNTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding

1.8k citations · 2.2k across the 11 of their papers we have counts for

collaborators

17 papers

cs.CV202072 cited

Multi-Domain Adversarial Feature Generalization for Person Re-Identification

Shan Lin, Chang-Tsun Li, Alex C. Kot

With the assistance of sophisticated training methods applied to single labeled datasets, the performance of fully-supervised person re-identification (Person Re-ID) has been impro…

cs.CV202082 cited

Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization

Haoliang Li, YuFei Wang, Renjie Wan +3

Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks. However, the recent advanced models still require access…

cs.LG20201 cited

Feature Distillation With Guided Adversarial Contrastive Learning

Tao Bai, Jinnan Chen, Jun Zhao +3

Deep learning models are shown to be vulnerable to adversarial examples. Though adversarial training can enhance model robustness, typical approaches are computationally expensive.…

cs.CV202098 cited

DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing

Rizhao Cai, Haoliang Li, Shiqi Wang +2

Inspired by the philosophy employed by human beings to determine whether a presented face example is genuine or not, i.e., to glance at the example globally first and then carefull…

cs.CV2020120 cited

Heterogeneous Domain Generalization via Domain Mixup

Yufei Wang, Haoliang Li, Alex C. Kot

One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is that they lack generalization capability. In this work, we focus on the problem of heterogeneous domain ge…

cs.CV20201 cited

Multi-Camera Trajectory Forecasting: Pedestrian Trajectory Prediction in a Network of Cameras

Olly Styles, Tanaya Guha, Victor Sanchez +1

We introduce the task of multi-camera trajectory forecasting (MCTF), where the future trajectory of an object is predicted in a network of cameras. Prior works consider forecasting…