Skimming and Scanning for Untrimmed Video Action Recognition

TL;DR

This paper introduces the Skim-Scan framework for untrimmed video action recognition, employing a divide-and-conquer clip selection strategy inspired by speed reading to improve accuracy and efficiency.

Contribution

It proposes a novel clip-level skimming and scanning approach that adaptively selects informative clips, outperforming existing methods in accuracy and computational efficiency.

Findings

Surpasses state-of-the-art accuracy on ActivityNet and mini-FCVID datasets.

Reduces computational complexity while maintaining high performance.

Effectively filters uninformative clips to focus on essential content.

Abstract

Video action recognition (VAR) is a primary task of video understanding, and untrimmed videos are more common in real-life scenes. Untrimmed videos have redundant and diverse clips containing contextual information, so sampling dense clips is essential. Recently, some works attempt to train a generic model to select the N most representative clips. However, it is difficult to model the complex relations from intra-class clips and inter-class videos within a single model and fixed selected number, and the entanglement of multiple relations is also hard to explain. Thus, instead of "only look once", we argue "divide and conquer" strategy will be more suitable in untrimmed VAR. Inspired by the speed reading mechanism, we propose a simple yet effective clip-level solution based on skim-scan techniques. Specifically, the proposed Skim-Scan framework first skims the entire video and drops those uninformative and misleading clips. For the remaining clips, it scans clips with diverse features gradually to drop redundant clips but cover essential content. The above strategies can adaptively select the necessary clips according to the difficulty of the different videos. To trade off the computational complexity and performance, we observe the similar statistical expression between lightweight and heavy networks, thus it supports us to explore the combination of them. Comprehensive experiments are performed on ActivityNet and mini-FCVID datasets, and results demonstrate that our solution surpasses the state-of-the-art performance in terms of both accuracy and efficiency.