TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness

2025-06-25

AI Summary

To address the limitations of supervised annotation dependency, computationally expensive attention mechanisms, and poor generalization in video summarization, this paper proposes a lightweight self-supervised framework. The method eliminates annotations, attention modules, and recurrent structures, instead modeling spatiotemporal dependencies solely via convolutional architectures. It introduces, for the first time, a Markov process–driven loss function that jointly optimizes frame representativeness and relative temporal ordering. Furthermore, a two-stage self-supervised paradigm is designed, integrating contrastive learning with reconstruction learning. Evaluated on SUMME and TVSum, the approach consistently outperforms existing unsupervised methods and matches the performance of state-of-the-art supervised models. It also demonstrates significantly improved cross-dataset generalization. These results validate the effectiveness and robustness of low-complexity, convolution-based architectures for video summarization.

Technology Category

• Natural Language Processing: Summarization
• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Video Understanding & Activity Analysis

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

The increasing ubiquity of video content and the corresponding demand for efficient access to meaningful information have elevated video summarization and video highlights as a vital research area. However, many state-of-the-art methods depend heavily either on supervised annotations or on attention-based models, which are computationally expensive and brittle in the face of distribution shifts that hinder cross-domain applicability across datasets. We introduce a pioneering self-supervised video summarization model that captures both spatial and temporal dependencies without the overhead of attention, RNNs, or transformers. Our framework integrates a novel set of Markov process-driven loss metrics and a two-stage self supervised learning paradigm that ensures both performance and efficiency. Our approach achieves state-of-the-art performance on the SUMME and TVSUM datasets, outperforming all existing unsupervised methods. It also rivals the best supervised models, demonstrating the potential for efficient, annotation-free architectures. This paves the way for more generalizable video summarization techniques and challenges the prevailing reliance on complex architectures.

Problem

• Self-supervised video summarization without costly annotations
• Overcoming computational expense of attention-based models
• Enhancing cross-domain applicability across datasets

Innovation

• Self-supervised learning without attention or RNNs
• Markov process-driven loss metrics
• Two-stage learning for efficiency and performance