5 papers
CueBench: Advancing Unified Understanding of Context-Aware Video Anomalies in Real-World
Yating Yu, Congqi Cao, Zhaoying Wang +6
How far are deep models from real-world video anomaly understanding (VAU)? Current works typically emphasize on detecting unexpected occurrences deviated from normal patterns or co…
Learning to Generalize without Bias for Open-Vocabulary Action Recognition
Yating Yu, Congqi Cao, Yifan Zhang +1
Leveraging the effective visual-text alignment and static generalizability from CLIP, recent video learners adopt CLIP initialization with further regularization or recombination f…
Task-Adapter++: Task-specific Adaptation with Order-aware Alignment for Few-shot Action Recognition
Congqi Cao, Peiheng Han, Yueran zhang +4
Large-scale pre-trained models have achieved remarkable success in language and image tasks, leading an increasing number of studies to explore the application of pre-trained image…
Vision and Intention Boost Large Language Model in Long-Term Action Anticipation
Congqi Cao, Lanshu Hu, Yating Yu +1
Long-term action anticipation (LTA) aims to predict future actions over an extended period. Previous approaches primarily focus on learning exclusively from video data but lack pri…
Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIP
Yating Yu, Congqi Cao, Yueran Zhang +3
Zero-shot action recognition (ZSAR) requires collaborative multi-modal spatiotemporal understanding. However, finetuning CLIP directly for ZSAR yields suboptimal performance, given…