activity
20202026
most citedDomain-Unified Prompt Representations for Source-Free Domain Generalization

9 citations · 14 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2026

DepthArb: Training-Free Depth-Arbitrated Generation for Occlusion-Robust Image Synthesis

Hongjin Niu, Jiahao Wang, Xirui Hu +4

Text-to-image models often struggle to synthesize correct occlusion relationships among multiple objects, especially in densely overlapping regions. Many training-free layout-guide…

cs.CV20241 cited

From Macro to Micro: Boosting micro-expression recognition via pre-training on macro-expression videos

Hanting Li, Hongjing Niu, Feng Zhao

Micro-expression recognition (MER) has drawn increasing attention in recent years due to its potential applications in intelligent medical and lie detection. However, the shortage…

cs.CV20231 cited

Frequency Decomposition to Tap the Potential of Single Domain for Generalization

Qingyue Yang, Hongjing Niu, Pengfei Xia +2

Domain generalization (DG), aiming at models able to work on multiple unseen domains, is a must-have characteristic of general artificial intelligence. DG based on single source do…

cs.CV20234 cited

CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition

Hanting Li, Hongjing Niu, Zhaoqing Zhu +1

Facial expression recognition (FER) is an essential task for understanding human behaviors. As one of the most informative behaviors of humans, facial expressions are often compoun…

cs.CV20229 cited

Domain-Unified Prompt Representations for Source-Free Domain Generalization

Hongjing Niu, Hanting Li, Feng Zhao +1

Domain generalization (DG), aiming to make models work on unseen domains, is a surefire way toward general artificial intelligence. Limited by the scale and diversity of current DG…

cs.CR20212 cited

Understanding the Error in Evaluating Adversarial Robustness

Pengfei Xia, Ziqiang Li, Hongjing Niu +1

Deep neural networks are easily misled by adversarial examples. Although lots of defense methods are proposed, many of them are demonstrated to lose effectiveness when against prop…