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Congqi Cao

7 accepted papers

2026

CueBench: Advancing Unified Understanding of Context-Aware Video Anomalies in Real-World

AAAI 2026technical

How far are deep models from real-world video anomaly understanding (VAU)? Current works typically emphasize detecting unexpected occurrences deviating from normal patterns or comprehending anomalous events with interpretable descriptions. However, they exhibit only a superficial comprehension of re

Cited by 0SourcePDFScholar
2025

Autoregressive Denoising Score Matching is a Good Video Anomaly Detector

ICCV 2025poster

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 growing interest, as it can model normal distribution and detect out-of-distribution anomalies. However, these likelihood-ba…

2025

Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIP

AAAI 2025technical

Zero-shot action recognition (ZSAR) requires collaborative multi-modal spatiotemporal understanding. However, finetuning CLIP directly for ZSAR yields suboptimal performance, given its inherent constraints in capturing essential temporal dynamics from both vision and text perspectives, especially wh…

2025

Learning to Generalize without Bias for Open-Vocabulary Action Recognition

ICCV 2025poster

Leveraging the effective visual-text alignment and static generalizability from CLIP, recent video learners adopt CLIP initialization with further regularization or recombination for generalization in open-vocabulary action recognition in-context. However, due to the static bias of CLIP, such video…

2023

A New Comprehensive Benchmark for Semi-Supervised Video Anomaly Detection and Anticipation

CVPR 2023poster

Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent surveillance system. However, an essential type of anomaly in VAD named scene-dependent anomaly has not received the attention of researchers. Moreover, there is no research investigating anomaly anticipation, a more…

2020

Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition

ECCV 2020poster

In skeleton-based action recognition, graph convolutional networks (GCNs) have achieved remarkable success. Nevertheless, how to efficiently model the spatial-temporal skeleton graph without introducing extra computation burden is a challenging problem for industrial deployment. In this paper, we re…

2017

Egocentric Gesture Recognition Using Recurrent 3D Convolutional Neural Networks With Spatiotemporal Transformer Modules

ICCV 2017spotlight

Gesture is a natural interface in interacting with wearable devices such as VR/AR helmet and glasses. The main challenge of gesture recognition in egocentric vision arises from the global camera motion caused by the spontaneous head movement of the device wearer. In this paper, we address the proble…

Cited by 122PDFScholar