ViKey: Enhancing Temporal Understanding in Videos via Visual Prompting

• URL: http://arxiv.org/abs/2603.23186v1
• Date: Tue, 24 Mar 2026 13:32:52 GMT
• Title: ViKey: Enhancing Temporal Understanding in Videos via Visual Prompting
• Abstract summary: Video Large Language Models (VideoLLMs) have enabled strong performance across diverse multimodal video tasks.<n>To reduce the high computational cost of processing dense video frames, efficiency-oriented methods such as frame selection have been widely adopted.<n>We introduce ViKey, a training-free framework that combines VP with a lightweight Keyword-Frame Mapping (KFM) module.
• Score: 17.594941754364484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in Video Large Language Models (VideoLLMs) have enabled strong performance across diverse multimodal video tasks. To reduce the high computational cost of processing dense video frames, efficiency-oriented methods such as frame selection have been widely adopted. While effective at minimizing redundancy, these methods often cause notable performance drops on tasks requiring temporal reasoning. Unlike humans, who can infer event progression from sparse visual cues, VideoLLMs frequently misinterpret temporal relations when intermediate frames are omitted. To address this limitation, we explore visual prompting (VP) as a lightweight yet effective way to enhance temporal understanding in VideoLLMs. Our analysis reveals that simply annotating each frame with explicit ordinal information helps the model perceive temporal continuity. This visual cue also supports frame-level referencing and mitigates positional ambiguity within a sparsely sampled sequence. Building on these insights, we introduce ViKey, a training-free framework that combines VP with a lightweight Keyword-Frame Mapping (KFM) module. KFM leverages frame indices as dictionary-like keys to link textual cues to the most relevant frames, providing explicit temporal anchors during inference. Despite its simplicity, our approach substantially improves temporal reasoning and, on some datasets, preserves dense-frame baseline performance with as few as 20% of frames.

Related papers

• Reasoning as Intersection: Consensus-Frame Alignment for Visual Focus in Video-MLLMs [81.04673240949074]
  Reinforcement learning has improved the reasoning ability of large language models.<n>Applying outcome-only rewards to video multimodal large language models provides limited guidance on which visual evidence should support the answer.<n>We introduce Consensus Frame GRPO, a temporal-annotation-free process-level reward framework for evidence-aware video reasoning.
  arXiv  Detail & Related papers  (2026-06-16T19:42:54Z)
• 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)
• KFFocus: Highlighting Keyframes for Enhanced Video Understanding [33.69757683688046]
  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.
  arXiv  Detail & Related papers  (2025-08-12T14:57:03Z)
• Less is More: Token-Efficient Video-QA via Adaptive Frame-Pruning and Semantic Graph Integration [24.337139909108117]
  "Less is more" phenomenon where excessive frames can paradoxically degrade performance due to context dilution.<n>"Visual echoes" yield significant temporal redundancy, which we term 'visual echoes'<n>"AFP" employs an adaptive hierarchical clustering algorithm on a fused ResNet-50 and CLIP feature space to identify and merge these echoes into single representatives.<n>Our full approach demonstrates a drastic reduction in required frames by up to 86.9% and total input tokens by up to 83.2%.
  arXiv  Detail & Related papers  (2025-08-05T11:31:55Z)
• 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)
• STORM: Token-Efficient Long Video Understanding for Multimodal LLMs [116.4479155699528]
  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)

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.