KFFocus: Highlighting Keyframes for Enhanced Video Understanding

• URL: http://arxiv.org/abs/2508.08989v1
• Date: Tue, 12 Aug 2025 14:57:03 GMT
• Title: KFFocus: Highlighting Keyframes for Enhanced Video Understanding
• Authors: Ming Nie, Chunwei Wang, Hang Xu, Li Zhang,
• Abstract summary: We propose KFFocus, a method designed to efficiently compress video tokens and emphasize the informative context present within video frames.<n>By assigning varying condensation ratios to frames based on their contextual relevance, KFFocus efficiently reduces token redundancy while preserving informative content details.<n>We also introduce a multimodal modeling module that encodes both the temporal relationships between video frames and the spatial structure within each frame.
• Score: 33.69757683688046
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recently, with the emergence of large language models, multimodal LLMs have demonstrated exceptional capabilities in image and video modalities. Despite advancements in video comprehension, the substantial computational demands of long video sequences lead current video LLMs (Vid-LLMs) to employ compression strategies at both the inter-frame level (e.g., uniform sampling of video frames) and intra-frame level (e.g., condensing all visual tokens of each frame into a limited number). However, this approach often neglects the uneven temporal distribution of critical information across frames, risking the omission of keyframes that contain essential temporal and semantic details. To tackle these challenges, we propose KFFocus, a method designed to efficiently compress video tokens and emphasize the informative context present within video frames. We substitute uniform sampling with a refined approach inspired by classic video compression principles to identify and capture keyframes based on their temporal redundancy. By assigning varying condensation ratios to frames based on their contextual relevance, KFFocus efficiently reduces token redundancy while preserving informative content details. Additionally, we introduce a spatiotemporal modeling module that encodes both the temporal relationships between video frames and the spatial structure within each frame, thus providing Vid-LLMs with a nuanced understanding of spatial-temporal dynamics. Extensive experiments on widely recognized video understanding benchmarks, especially long video scenarios, demonstrate that KFFocus significantly outperforms existing methods, achieving substantial computational efficiency and enhanced accuracy.

Related papers

• CoPE-VideoLM: Codec Primitives For Efficient Video Language Models [56.76440182038839]
  Video Language Models (VideoLMs) empower AI systems to understand temporal dynamics in videos.<n>Current methods use sampling which can miss both macro-level events and micro-level details due to the sparse temporal coverage.<n>We propose to leverage video primitives which encode video redundancy and sparsity without requiring expensive full-image encoding for most frames.
  arXiv  Detail & Related papers  (2026-02-13T18:57:31Z)
• 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)
• From Captions to Keyframes: KeyScore for Multimodal Frame Scoring and Video-Language Understanding [1.3856027745141806]
  KeyScore is a caption-aware frame scoring method that combines semantic similarity to captions, temporal representativeness, and contextual drop impact.<n>Our method enhances both efficiency and performance, enabling scalable and caption-grounded video understanding.
  arXiv  Detail & Related papers  (2025-10-07T23:02:27Z)
• FrameMind: Frame-Interleaved Video Reasoning via Reinforcement Learning [65.42201665046505]
  Current video understanding models rely on fixed frame sampling strategies, processing predetermined visual inputs regardless of the specific reasoning requirements of each question.<n>This static approach limits their ability to adaptively gather visual evidence, leading to suboptimal performance on tasks that require broad temporal coverage or fine-grained spatial detail.<n>We introduce FrameMind, an end-to-end framework trained with reinforcement learning that enables models to dynamically request visual information during reasoning through Frame-Interleaved Chain-of-Thought (FiCOT)<n>Unlike traditional approaches, FrameMind operates in multiple turns where the model alternates between textual reasoning and active visual perception, using tools to extract
  arXiv  Detail & Related papers  (2025-09-28T17:59:43Z)
• 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)
• Threading Keyframe with Narratives: MLLMs as Strong Long Video Comprehenders [62.58375366359421]
  Multimodal Large Language Models (MLLMs) for long video understanding remains a challenging problem.<n>Traditional uniform sampling leads to selection of irrelevant content.<n>Post-training MLLMs on thousands of frames imposes a substantial computational burden.<n>We propose threadings with narratives (Nar-KFC) to facilitate effective and efficient long video perception.
  arXiv  Detail & Related papers  (2025-05-30T03:04:28Z)
• STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding [48.12128042470839]
  We propose an integrated Spatial-TempOral dynamic Prompting (STOP) model.<n>It consists of two complementary modules, the intra-frame spatial prompting and inter-frame temporal prompting.<n>STOP consistently achieves superior performance against state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-03-20T09:16:20Z)
• Token-Efficient Long Video Understanding for Multimodal LLMs [101.70681093383365]
  STORM is a novel architecture incorporating a dedicated temporal encoder between the image encoder and the Video-LLMs.<n>We show that STORM achieves state-of-the-art results across various long video understanding benchmarks.
  arXiv  Detail & Related papers  (2025-03-06T06:17:38Z)
• Beyond Training: Dynamic Token Merging for Zero-Shot Video Understanding [11.211803499867639]
  We propose DYTO, a novel dynamic token merging framework for zero-shot video understanding.<n> DYTO integrates a hierarchical frame selection and a bipartite token merging strategy to dynamically cluster key frames and selectively compress token sequences.<n>Experiments demonstrate the effectiveness of DYTO, achieving superior performance compared to both fine-tuned and training-free methods.
  arXiv  Detail & Related papers  (2024-11-21T18:30:11Z)
• 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.