2 citations · 5 across the 5 of their papers we have counts for
7 papers
A Unified Score Matching Paradigm for Video Anomaly Detection and Anticipation
Congqi Cao, Zhenhe Liang, Hanwen Zhang +4
Video anomaly detection (VAD) is a fundamental and safety-critical task in computer vision. Recent generative approaches detect anomalies from a distributional perspective, but rem…
Autoregressive Denoising Score Matching is a Good Video Anomaly Detector
Hanwen Zhang, Congqi Cao, Qinyi Lv +2
Video anomaly detection (VAD) is an important computer vision problem. Thanks to the mode coverage capabilities of generative models, the likelihood-based paradigm is catching grow…
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…
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…
Task-Adapter: Task-specific Adaptation of Image Models for Few-shot Action Recognition
Congqi Cao, Yueran Zhang, Yating Yu +3
Existing works in few-shot action recognition mostly fine-tune a pre-trained image model and design sophisticated temporal alignment modules at feature level. However, simply fully…
VS-TransGRU: A Novel Transformer-GRU-based Framework Enhanced by Visual-Semantic Fusion for Egocentric Action Anticipation
Congqi Cao, Ze Sun, Qinyi Lv +2
Egocentric action anticipation is a challenging task that aims to make advanced predictions of future actions from current and historical observations in the first-person view. Mos…
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization
Congqi Cao, Yajuan Li, Qinyi Lv +2
Few-shot learning aims to recognize instances from novel classes with few labeled samples, which has great value in research and application. Although there has been a lot of work…