HAS: Highlight-guided Attention Steering for Multimodal LLM Video Summarization

• URL: http://arxiv.org/abs/2607.17994v1
• Date: Mon, 20 Jul 2026 14:27:06 GMT
• Title: HAS: Highlight-guided Attention Steering for Multimodal LLM Video Summarization
• Abstract summary: We propose HAS, a Highlight-guided Attention Steering method for video summarization.<n>We evaluate HAS on a variety of benchmarks, and it has shown convincing performance in video summarization.
• Score: 16.58089288863005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video understanding has become more and more important with the growth of Artificial Intelligence (AI) for video generation. Recently, Multimodal Large Language Model(M-LLM) has shown its capability in video understanding. Video summarization, a specific domain of video understanding, has proven its importance for efficient navigation and retrieval. Both video understanding and video summarization require a good selection of key frames in a video. Current video summarization methods heavily focus on the selected key frames and correlated segment captions. However, existing approaches overlook the perspective of treating the importance of the frames globally. We argue that using discrete selected frames for summarization will not only reduce the understanding coherence, but also lost important information in the video, as well as wasting the original capacity of the MLLMs. In this paper, we propose HAS, a Highlight-guided Attention Steering method for video summarization. We consider a challenging but practical setting where the video given to MLLMs for summarize should be continuous but with highlight guidance. HAS mainly consists of two parts: The first part is to find a continuous frame-level highlight distribution for the video globally. The second part is to apply the highlight distribution as an attention steering vector for the MLLM, targeting a better understanding of the video, and thus during the model inference time, putting more attention on the highlighted frames, while avoiding lost entire information on less highlighted frames through putting less attention instead of forgetting them. We evaluated HAS on a variety of benchmarks, and it has shown convincing performance in video summarization.

Related papers

• Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding [98.3098451637867]
  Video-MME-v2 is a comprehensive benchmark designed to rigorously evaluate the robustness and faithfulness of video understanding.<n>To guarantee data quality, Video-MME-v2 is constructed through a rigorously controlled human annotation pipeline.
  arXiv  Detail & Related papers  (2026-04-06T17:59:56Z)
• FocusGraph: Graph-Structured Frame Selection for Embodied Long Video Question Answering [0.6107667071306521]
  We develop FocusGraph, a framework for question answering over long egocentric videos.<n>We use a lightweight trainable Scene-Caption LLM Selector that selects query-relevant clips based on their graph-based captions.<n>We then design a training-free Patch-wise Sparse-Flow Retention (PSFR) method to select textuals from the resulting sequence of clips.
  arXiv  Detail & Related papers  (2026-03-04T18:14:00Z)
• SlowFocus: Enhancing Fine-grained Temporal Understanding in Video LLM [36.28285195488772]
  Large language models (LLMs) have demonstrated exceptional capabilities in text understanding.<n>Vid-LLMs struggle to simultaneously retain high-quality frame-level semantic information.<n>This limitation hinders the advancement of Vid-LLMs towards fine-grained video understanding.
  arXiv  Detail & Related papers  (2026-02-03T14:39:16Z)
• Video-MTR: Reinforced Multi-Turn Reasoning for Long Video Understanding [33.58579390725519]
  Video-MTR is a reinforced multi-turn reasoning framework designed to enable iterative key video segment selection and question comprehension.<n>Unlike traditional video reasoning pipeline, which generate predictions in a single turn, Video-MTR performs reasoning in multiple turns.<n>To ensure intermediate reasoning process, we introduce a novel gated bi-level reward system.
  arXiv  Detail & Related papers  (2025-08-28T06:55:08Z)
• Episodic Memory Representation for Long-form Video Understanding [52.33907540905242]
  Large Video Language Models excel at general video understanding but struggle with long-form context window limits.<n>We introduce Video-EM, a training free framework inspired by the principles of human memory.<n>Video-EM achieves performance gains of 4-9 percent over respective baselines while utilizing fewer frames.
  arXiv  Detail & Related papers  (2025-08-13T04:33:07Z)
• ViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning [68.76048244253582]
  We introduce ViaRL, the first framework to leverage rule-based reinforcement learning (RL) for optimizing frame selection in video understanding.<n>ViaRL utilizes the answer accuracy of a downstream model as a reward signal to train a frame selector through trial-and-error.<n>ViaRL consistently delivers superior temporal grounding performance and robust generalization across diverse video understanding tasks.
  arXiv  Detail & Related papers  (2025-05-21T12:29:40Z)
• Video Summarization with Large Language Models [41.51242348081083]
  We propose a new video summarization framework that leverages the capabilities of recent Large Language Models (LLMs)<n>Our method, dubbed LLM-based Video Summarization (LLMVS), translates video frames into a sequence of captions using a Muti-modal Large Language Model (MLLM)<n>Our experimental results demonstrate the superiority of the proposed method over existing ones in standard benchmarks.
  arXiv  Detail & Related papers  (2025-04-15T13:56:14Z)
• VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding [63.075626670943116]
  We introduce a cutting-edge framework, VaQuitA, designed to refine the synergy between video and textual information. At the data level, instead of sampling frames uniformly, we implement a sampling method guided by CLIP-score rankings. At the feature level, we integrate a trainable Video Perceiver alongside a Visual-Query Transformer.
  arXiv  Detail & Related papers  (2023-12-04T19:48:02Z)
• Conditional Modeling Based Automatic Video Summarization [70.96973928590958]
  The aim of video summarization is to shorten videos automatically while retaining the key information necessary to convey the overall story. Video summarization methods rely on visual factors, such as visual consecutiveness and diversity, which may not be sufficient to fully understand the content of the video. A new approach to video summarization is proposed based on insights gained from how humans create ground truth video summaries.
  arXiv  Detail & Related papers  (2023-11-20T20:24:45Z)
• Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought [62.619076257298204]
  We motivate framing video reasoning as the sequential understanding of a small number of video reasonings. We introduce VIP, an inference-time challenge dataset designed to explore models' reasoning capabilities through video chain-of-thought. We benchmark GPT-4, GPT-3, and VICUNA on VIP, demonstrate the performance gap in complex video reasoning tasks, and encourage future work.
  arXiv  Detail & Related papers  (2023-05-23T10:26:42Z)
• MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization [61.69587867308656]
  We propose a multimodal hierarchical shot-aware convolutional network, denoted as MHSCNet, to enhance the frame-wise representation. Based on the learned shot-aware representations, MHSCNet can predict the frame-level importance score in the local and global view of the video.
  arXiv  Detail & Related papers  (2022-04-18T14:53:33Z)

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.