Paper ID: 2410.14993

Making Every Frame Matter: Continuous Video Understanding for Large Models via Adaptive State Modeling

Hao Wu, Donglin Bai, Shiqi Jiang, Qianxi Zhang, Yifan Yang, Ting Cao, Fengyuan Xu

Video understanding has become increasingly important with the rise of multi-modality applications. Understanding continuous video poses considerable challenges due to the fast expansion of streaming video, which contains multi-scale and untrimmed events. We introduce a novel system, C-VUE, to overcome these issues through adaptive state modeling. C-VUE has three key designs. The first is a long-range history modeling technique that uses a video-aware approach to retain historical video information. The second is a spatial redundancy reduction technique, which enhances the efficiency of history modeling based on temporal relations. The third is a parallel training structure that incorporates the frame-weighted loss to understand multi-scale events in long videos. Our C-VUE offers high accuracy and efficiency. It runs at speeds >30 FPS on typical edge devices and outperforms all baselines in accuracy. Moreover, applying C-VUE to a video foundation model as a video encoder in our case study resulted in a 0.46-point enhancement (on a 5-point scale) on the in-distribution dataset, and an improvement ranging from 1.19\% to 4\% on zero-shot datasets.

Submitted: Oct 19, 2024