activity
20222025
most citedIGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition

3 citations · 5 across the 9 of their papers we have counts for

collaborators

7 papers

cs.CV2023

Unsupervised Domain Adaptation via Domain-Adaptive Diffusion

Duo Peng, Qiuhong Ke, Yinjie Lei +1

Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain. Inspired by diffusion models wh…

cs.CV2023

Diffusion-based Image Translation with Label Guidance for Domain Adaptive Semantic Segmentation

Duo Peng, Ping Hu, Qiuhong Ke +1

Translating images from a source domain to a target domain for learning target models is one of the most common strategies in domain adaptive semantic segmentation (DASS). However,…

cs.CR2023

GradMDM: Adversarial Attack on Dynamic Networks

Jianhong Pan, Lin Geng Foo, Qichen Zheng +4

Dynamic neural networks can greatly reduce computation redundancy without compromising accuracy by adapting their structures based on the input. In this paper, we explore the robus…

cs.CV2023

Progressive Channel-Shrinking Network

Jianhong Pan, Siyuan Yang, Lin Geng Foo +4

Currently, salience-based channel pruning makes continuous breakthroughs in network compression. In the realization, the salience mechanism is used as a metric of channel salience…

cs.CV20221 cited

Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition

Tianjiao Li, Lin Geng Foo, Qiuhong Ke +4

The goal of fine-grained action recognition is to successfully discriminate between action categories with subtle differences. To tackle this, we derive inspiration from the human…

cs.CV20223 cited

IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition

Yunsheng Pang, Qiuhong Ke, Hossein Rahmani +2

Human interaction recognition is very important in many applications. One crucial cue in recognizing an interaction is the interactive body parts. In this work, we propose a novel…