Improving Spatial-Temporal Reasoning in Video-Language Models with Structured Video Prompting

• URL: http://arxiv.org/abs/2608.28666v1
• Date: Sun, 23 Aug 2026 20:16:09 GMT
• Title: Improving Spatial-Temporal Reasoning in Video-Language Models with Structured Video Prompting
• Abstract summary: Video-language models (VLMs) remain brittle on tasks that require tracking events over time and grounding answers in specific spatial regions.<n>We introduce structured video prompting, a training-free inference-time method that augments the input video with lightweight spatial structure and temporal structure.<n>We evaluate this approach on two complementary video benchmarks and two open video-language models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video-language models (VLMs) remain brittle on tasks that require tracking events over time and grounding answers in specific spatial regions. We propose that part of this limitation can be addressed through better organization of visual evidence at inference time. We introduce structured video prompting, a training-free inference-time method that augments the input video with lightweight spatial structure and temporal structure, providing explicit anchors for organizing evidence across space and time without changing model weights or decoding and without altering the question prompt in the main comparison. We evaluate this approach on two complementary video benchmarks and two open video-language models. Across these settings, structured inputs improve performance in several cases, with gains varying by model and task. Our findings suggest that some failures of VLMs arise not only from reasoning capacity, but also from how video evidence is presented at inference time. These results highlight structured video prompting as a simple and practical direction for improving video understanding.

Related papers

• Training-Free Temporal Abstraction for General Video Understanding [12.051446549851844]
  We present STITCH, a training-free method that divides a video into semantically meaningful temporal chunks.<n>We evaluate STITCH on generic event boundary detection, language-based moment retrieval, and frame selection for long-video VLM reasoning.
  arXiv  Detail & Related papers  (2026-08-28T05:10:54Z)
• VideoWeave: A Data-Centric Approach for Efficient Video Understanding [54.5804686337209]
  We present VideoWeave, a simple yet effective approach to improve data efficiency by constructing synthetic long-context training samples.<n>VideoWeave reorganizes available video-text pairs to expand temporal diversity within fixed compute.<n>Our results highlight that reorganizing training data, rather than altering architectures, may offer a simple and scalable path for training video-language models.
  arXiv  Detail & Related papers  (2026-01-09T20:55:26Z)
• VideoPerceiver: Enhancing Fine-Grained Temporal Perception in Video Multimodal Large Language Models [9.896951371033229]
  VideoPerceiver is a video multimodal large language model (VMLLM) that enhances fine-grained perception in video understanding.<n>We construct "key-information-missing" videos by extracting event-action keywords from captions, identifying corresponding key frames, and replacing them with adjacent frames.<n> Experiments show VideoPerceiver substantially outperforms state-of-the-art VMLLMs on fine-grained action understanding and rare event captioning benchmarks.
  arXiv  Detail & Related papers  (2025-11-24T06:57:26Z)
• 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)
• 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)
• Low-Cost Test-Time Adaptation for Robust Video Editing [4.707015344498921]
  Video editing is a critical component of content creation that transforms raw footage into coherent works aligned with specific visual and narrative objectives.<n>Existing approaches face two major challenges: temporal inconsistencies due to failure in capturing complex motion patterns, and overfitting to simple prompts arising from limitations in UNet backbone architectures.<n>We present Vid-TTA, a lightweight test-time adaptation framework that personalizes optimization for each test video during inference through self-supervised auxiliary tasks.
  arXiv  Detail & Related papers  (2025-07-29T14:31:17Z)
• Universal Video Temporal Grounding with Generative Multi-modal Large Language Models [59.781211641591405]
  This paper presents a computational model for universal video temporal grounding, which accurately localizes temporal moments in videos based on natural language queries.<n>We propose UniTime, a robust and universal video grounding model leveraging the strong vision-language understanding capabilities of generative Multi-modal Large Language Models (MLLMs)<n>Our model effectively handles videos of diverse views, genres, and lengths while comprehending complex language queries.
  arXiv  Detail & Related papers  (2025-06-23T17:53:18Z)
• 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)
• InternVideo2: Scaling Foundation Models for Multimodal Video Understanding [51.129913789991924]
  InternVideo2 is a new family of video foundation models (FM) that achieve state-of-the-art results in video recognition, video-speech tasks, and video-centric tasks. Our core design is a progressive training approach that unifies the masked video modeling, cross contrastive learning, and prediction token, scaling up to 6B video size.
  arXiv  Detail & Related papers  (2024-03-22T17:57:42Z)
• VidCoM: Fast Video Comprehension through Large Language Models with Multimodal Tools [44.78291853329394]
  textbfVidCoM is a fast adaptive framework that leverages Large Language Models (LLMs) to reason about videos using lightweight visual tools. An InsOVER algorithm locates the corresponding video events based on an efficient Hungarian matching between decompositions of linguistic instructions and video events.
  arXiv  Detail & Related papers  (2023-10-16T17:05:56Z)
• Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding [112.3913646778859]
  We propose a simple yet effective video-language modeling framework, S-ViLM. It includes two novel designs, inter-clip spatial grounding and intra-clip temporal grouping, to promote learning region-object alignment and temporal-aware features. S-ViLM surpasses the state-of-the-art methods substantially on four representative downstream tasks.
  arXiv  Detail & Related papers  (2023-03-28T22:45:07Z)
• Revisiting the "Video" in Video-Language Understanding [56.15777956496518]
  We propose the atemporal probe (ATP), a new model for video-language analysis. We characterize the limitations and potential of current video-language benchmarks. We show that effectively integrating ATP into full video-level temporal models can improve efficiency and state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2022-06-03T17:57:33Z)

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.