activity
20162024
most citedContrastive Learning of Person-independent Representations for Facial Action Unit Detection

15 citations · 45 across the 18 of their papers we have counts for

collaborators

18 papers

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.CV20241 cited

T2IShield: Defending Against Backdoors on Text-to-Image Diffusion Models

Zhongqi Wang, Jie Zhang, Shiguang Shan +1

While text-to-image diffusion models demonstrate impressive generation capabilities, they also exhibit vulnerability to backdoor attacks, which involve the manipulation of model ou…

cs.CV20242 cited

HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention

Xiaolong Tang, Meina Kan, Shiguang Shan +3

Predicting the trajectories of road agents is essential for autonomous driving systems. The recent mainstream methods follow a static paradigm, which predicts the future trajectory…

cs.CV2024

GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

Hao Lu, Xuesong Niu, Jiyao Wang +12

Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in l…

cs.CV2024

Task Attribute Distance for Few-Shot Learning: Theoretical Analysis and Applications

Minyang Hu, Hong Chang, Zong Guo +3

Few-shot learning (FSL) aims to learn novel tasks with very few labeled samples by leveraging experience from \emph{related} training tasks. In this paper, we try to understand FSL…

cs.CV202415 cited

Contrastive Learning of Person-independent Representations for Facial Action Unit Detection

Yong Li, Shiguang Shan

Facial action unit (AU) detection, aiming to classify AU present in the facial image, has long suffered from insufficient AU annotations. In this paper, we aim to mitigate this dat…