KFFocus: Highlighting Keyframes for Enhanced Video Understanding

2025-08-12

AI Summary

Current video large language models (Vid-LLMs) suffer from critical temporal/semantic information loss in long-video understanding due to uniform frame sampling and fixed-frame token compression. To address this, we propose an adaptive spatio-temporal aware video token compression framework. Methodologically: (1) key frames are dynamically selected via time-redundancy analysis; (2) a context-aware intra-frame token compression strategy is introduced, enabling semantic-importance–driven differential compression per frame; and (3) a lightweight spatio-temporal joint modeling module explicitly captures inter-frame temporal dependencies and intra-frame spatial structure. Evaluated on multiple mainstream long-video understanding benchmarks, our approach significantly outperforms existing state-of-the-art methods—achieving higher accuracy while maintaining high computational efficiency. Notably, it demonstrates superior robustness and generalization on ultra-long videos, validating its effectiveness in preserving fine-grained spatio-temporal semantics under stringent token budget constraints.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Identify and capture keyframes to avoid omitting essential video details
• Efficiently compress video tokens while preserving informative content
• Enhance spatial-temporal understanding in video language models

Innovation

• Keyframe identification via temporal redundancy analysis
• Dynamic token condensation based on contextual relevance
• Spatiotemporal modeling for nuanced video understanding