activity
20162024
most citedGenerative Domain Adaptation for Face Anti-Spoofing

67 citations · 215 across the 28 of their papers we have counts for

collaborators

28 papers

cs.CV2024

Dual Relation Mining Network for Zero-Shot Learning

Jinwei Han, Yingguo Gao, Zhiwen Lin +4

Zero-shot learning (ZSL) aims to recognize novel classes through transferring shared semantic knowledge (e.g., attributes) from seen classes to unseen classes. Recently, attention-…

cs.CV2024

Anchor-based Robust Finetuning of Vision-Language Models

Jinwei Han, Zhiwen Lin, Zhongyisun Sun +5

We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as…

cs.CV2024

SDPose: Tokenized Pose Estimation via Circulation-Guide Self-Distillation

Sichen Chen, Yingyi Zhang, Siming Huang +7

Recently, transformer-based methods have achieved state-of-the-art prediction quality on human pose estimation(HPE). Nonetheless, most of these top-performing transformer-based mod…

cs.CV20242 cited

Test-Time Domain Generalization for Face Anti-Spoofing

Qianyu Zhou, Ke-Yue Zhang, Taiping Yao +3

Face Anti-Spoofing (FAS) is pivotal in safeguarding facial recognition systems against presentation attacks. While domain generalization (DG) methods have been developed to enhance…

cs.CV20241 cited

Privacy-Preserving Face Recognition Using Trainable Feature Subtraction

Yuxi Mi, Zhizhou Zhong, Yuge Huang +6

The widespread adoption of face recognition has led to increasing privacy concerns, as unauthorized access to face images can expose sensitive personal information. This paper expl…

cs.CL20242 cited

Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models

Didi Zhu, Zhongyi Sun, Zexi Li +5

Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a sig…