Cross-category Video Highlight Detection via Set-based Learning

TL;DR

This paper introduces a novel set-based learning framework for cross-category video highlight detection, effectively transferring highlight knowledge from source to target categories without direct annotations.

Contribution

It proposes a dual-learner framework with a set-based learning module and knowledge distillation for improved cross-category highlight detection.

Findings

The set-based learning module outperforms conventional pair-based methods.

The proposed DL-VHD method surpasses five typical UDA algorithms in experiments.

Extensive experiments on three benchmarks validate the effectiveness of the approach.

Abstract

Autonomous highlight detection is crucial for enhancing the efficiency of video browsing on social media platforms. To attain this goal in a data-driven way, one may often face the situation where highlight annotations are not available on the target video category used in practice, while the supervision on another video category (named as source video category) is achievable. In such a situation, one can derive an effective highlight detector on target video category by transferring the highlight knowledge acquired from source video category to the target one. We call this problem cross-category video highlight detection, which has been rarely studied in previous works. For tackling such practical problem, we propose a Dual-Learner-based Video Highlight Detection (DL-VHD) framework. Under this framework, we first design a Set-based Learning module (SL-module) to improve the conventional pair-based learning by assessing the highlight extent of a video segment under a broader context. Based on such learning manner, we introduce two different learners to acquire the basic distinction of target category videos and the characteristics of highlight moments on source video category, respectively. These two types of highlight knowledge are further consolidated via knowledge distillation. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed SL-module, and the DL-VHD method outperforms five typical Unsupervised Domain Adaptation (UDA) algorithms on various cross-category highlight detection tasks. Our code is available at https://github.com/ChrisAllenMing/Cross_Category_Video_Highlight .