activity
20222024
most citedA Comprehensive Survey on Segment Anything Model for Vision and Beyond

47 citations · 68 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV20241 cited

TRRG: Towards Truthful Radiology Report Generation With Cross-modal Disease Clue Enhanced Large Language Model

Yuhao Wang, Chao Hao, Yawen Cui +4

The vision-language modeling capability of multi-modal large language models has attracted wide attention from the community. However, in medical domain, radiology report generatio…

cs.CV20241 cited

Segment Anything for Videos: A Systematic Survey

Chunhui Zhang, Yawen Cui, Weilin Lin +4

The recent wave of foundation models has witnessed tremendous success in computer vision (CV) and beyond, with the segment anything model (SAM) having sparked a passion for explori…

cs.CV20232 cited

Visual Prompt Flexible-Modal Face Anti-Spoofing

Zitong Yu, Rizhao Cai, Yawen Cui +2

Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, multimodal face data colle…

cs.CV202347 cited

A Comprehensive Survey on Segment Anything Model for Vision and Beyond

Chunhui Zhang, Li Liu, Yawen Cui +4

Artificial intelligence (AI) is evolving towards artificial general intelligence, which refers to the ability of an AI system to perform a wide range of tasks and exhibit a level o…

cs.CV20231 cited

Rehearsal-Free Domain Continual Face Anti-Spoofing: Generalize More and Forget Less

Rizhao Cai, Yawen Cui, Zhi Li +4

Face Anti-Spoofing (FAS) is recently studied under the continual learning setting, where the FAS models are expected to evolve after encountering the data from new domains. However…

cs.CV20235 cited

Generalized Few-Shot Continual Learning with Contrastive Mixture of Adapters

Yawen Cui, Zitong Yu, Rizhao Cai +3

The goal of Few-Shot Continual Learning (FSCL) is to incrementally learn novel tasks with limited labeled samples and preserve previous capabilities simultaneously, while current F…