TRIMMER: A New Paradigm for Video Summarization through Self-Supervised Reinforcement Learning

• URL: http://arxiv.org/abs/2605.01659v1
• Date: Sun, 03 May 2026 00:55:02 GMT
• Title: TRIMMER: A New Paradigm for Video Summarization through Self-Supervised Reinforcement Learning
• Abstract summary: We propose a novel self-supervised reinforcement learning framework for video summarization.<n>We show it achieves state-of-the-art performance among unsupervised and self-supervised methods.<n>It remains competitive with leading supervised approaches, highlighting its effectiveness for scalable and general video summarization.
• Score: 8.864968998286772
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth of video content across domains such as surveillance, education, and social media has made efficient content understanding increasingly critical. Video summarization addresses this challenge by generating concise yet semantically meaningful representations, but existing approaches often rely on expensive manual annotations, struggle to generalize across domains, and incur significant computational costs due to complex architectures. Moreover, unsupervised and weakly supervised methods typically underperform compared to supervised counterparts in capturing long-range temporal dependencies and semantic structure. In this work, we propose TRIMMER (Temporal Relative Information Maximization for Multi-objective Efficient Reinforcement), a novel self-supervised reinforcement learning framework for video summarization. TRIMMER operates in two stages: it first learns robust representations via self-supervised learning and then performs spatio-temporal decision making through reinforcement learning guided by information-theoretic reward functions. Unlike prior approaches that rely on similarity-based objectives, our method introduces entropy-based metrics to capture higher-order temporal dynamics and semantic diversity, while computing rewards directly over selected frame indices to improve computational efficiency. Extensive experiments on standard benchmarks demonstrate that TRIMMER achieves state-of-the-art performance among unsupervised and self-supervised methods, while remaining competitive with leading supervised approaches, highlighting its effectiveness for scalable and generalizable video summarization.

Related papers

• InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning [61.87294123934038]
  We present InternVideo3, a framework enhancing multimodal understanding in video tasks.<n>MCR treats understanding as a closed-loop process over a shared, evolving context.<n>We show InternVideo3 achieves strong performance on benchmarks like Video-MME, MLVU, and Ego.
  arXiv  Detail & Related papers  (2026-06-10T15:17:08Z)
• Semantic-Guided Unsupervised Video Summarization [5.891053607698674]
  We propose a novel Semantic-Guided Unsupervised Video Summarization method.<n>Specifically, we design a novel frame-level semantic alignment attention selector, which guides the Transformer-based generator within the adversarial framework to better reconstruct videos.<n>In addition, we adopt an incremental training strategy to progressively update the model components, effectively mitigating the instability of GAN training.
  arXiv  Detail & Related papers  (2026-01-21T08:53:29Z)
• Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models [78.32948112203228]
  Video understanding represents the most challenging frontier in computer vision.<n>Recent emergence of Video-Large Multitemporal Models has demonstrated remarkable capabilities in video understanding tasks.<n>Survey aims to provide researchers and practitioners with a unified framework for advancing Video-LMM capabilities.
  arXiv  Detail & Related papers  (2025-10-06T17:10:44Z)
• TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness [9.374702244811303]
  We introduce a self-supervised video summarization model that captures both spatial and temporal dependencies without the overhead of attention, RNNs, or transformers.<n>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.
  arXiv  Detail & Related papers  (2025-06-25T16:27:38Z)
• Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency [56.475612147721264]
  We propose a dual-reward formulation that supervises both semantic and temporal reasoning through discrete and continuous reward signals.<n>We evaluate our approach across eight representative video understanding tasks, including VideoQA, Temporal Video Grounding, and Grounded VideoQA.<n>Results underscore the importance of reward design and data selection in advancing reasoning-centric video understanding with MLLMs.
  arXiv  Detail & Related papers  (2025-06-02T17:28:26Z)
• STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training [87.58996020705258]
  Video Large Language Models (Video-LLMs) have recently shown strong derivation in basic video understanding tasks.<n>Video-LLMs struggle with compositional reasoning that requires multi-step explicit-temporal inference across object relations, interactions and events.<n>We propose STEP, a novel graph-guided self-training method that enables VideoLLMs to generate reasoning-rich finetuning data from any raw videos to improve itself.
  arXiv  Detail & Related papers  (2024-11-29T11:54:55Z)
• Efficient Transfer Learning for Video-language Foundation Models [13.166348605993292]
  We propose a parameter-efficient Multi-modalpatio Ssupervised-Temporal Adapter (MSTA) to enhance alignment between textual and visual representations.<n>We evaluate the effectiveness of our approach across four tasks: zero-shot transfer, few-shot learning, base-to-novel generalization, and fully-Temporal learning.
  arXiv  Detail & Related papers  (2024-11-18T01:25:58Z)
• Visual Self-paced Iterative Learning for Unsupervised Temporal Action Localization [50.48350210022611]
  We present a novel self-paced iterative learning model to enhance clustering and localization training simultaneously.<n>We design two (constant- and variable- speed) incremental instance learning strategies for easy-to-hard model training, thus ensuring the reliability of these video pseudolabels.
  arXiv  Detail & Related papers  (2023-12-12T16:00:55Z)
• Self-supervised Video Object Segmentation [76.83567326586162]
  The objective of this paper is self-supervised representation learning, with the goal of solving semi-supervised video object segmentation (a.k.a. dense tracking) We make the following contributions: (i) we propose to improve the existing self-supervised approach, with a simple, yet more effective memory mechanism for long-term correspondence matching; (ii) by augmenting the self-supervised approach with an online adaptation module, our method successfully alleviates tracker drifts caused by spatial-temporal discontinuity; (iv) we demonstrate state-of-the-art results among the self-supervised approaches on DAVIS-2017 and YouTube
  arXiv  Detail & Related papers  (2020-06-22T17:55:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.