LeAdQA: LLM-Driven Context-Aware Temporal Grounding for Video Question Answering

• URL: http://arxiv.org/abs/2507.14784v1
• Date: Sun, 20 Jul 2025 01:57:00 GMT
• Title: LeAdQA: LLM-Driven Context-Aware Temporal Grounding for Video Question Answering
• Authors: Xinxin Dong, Baoyun Peng, Haokai Ma, Yufei Wang, Zixuan Dong, Fei Hu, Xiaodong Wang,
• Abstract summary: We introduce LeAdQA, an innovative approach that bridges these gaps through synergizing causal-aware query refinement with fine-grained visual grounding.<n> Experiments on NExT-QA, IntentQA, and NExT-GQA demonstrate that our method's precise visual grounding substantially enhances the understanding of video-question relationships.
• Score: 10.060267989615813
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Question Answering (VideoQA) requires identifying sparse critical moments in long videos and reasoning about their causal relationships to answer semantically complex questions. While recent advances in multimodal learning have improved alignment and fusion, current approaches remain limited by two prevalent but fundamentally flawed strategies: (1) task-agnostic sampling indiscriminately processes all frames, overwhelming key events with irrelevant content; and (2) heuristic retrieval captures superficial patterns but misses causal-temporal structures needed for complex reasoning. To address these challenges, we introduce LeAdQA, an innovative approach that bridges these gaps through synergizing causal-aware query refinement with fine-grained visual grounding. Our method first leverages LLMs to reformulate question-option pairs, resolving causal ambiguities and sharpening temporal focus. These refined queries subsequently direct a temporal grounding model to precisely retrieve the most salient segments, complemented by an adaptive fusion mechanism dynamically integrating the evidence to maximize relevance. The integrated visual-textual cues are then processed by an MLLM to generate accurate, contextually-grounded answers. Experiments on NExT-QA, IntentQA, and NExT-GQA demonstrate that our method's precise visual grounding substantially enhances the understanding of video-question relationships, achieving state-of-the-art (SOTA) performance on complex reasoning tasks while maintaining computational efficiency.

Related papers

• Video SimpleQA: Towards Factuality Evaluation in Large Video Language Models [69.68265487134686]
  Video SimpleQA is the first comprehensive benchmark tailored for factuality evaluation of LVLMs.<n>Our work distinguishes from existing video benchmarks through the following key features.<n>Answers are crafted as unambiguous and definitively correct in a short format.
  arXiv  Detail & Related papers  (2025-03-24T17:46:09Z)
• Admitting Ignorance Helps the Video Question Answering Models to Answer [82.22149677979189]
  We argue that models often establish shortcuts, resulting in spurious correlations between questions and answers.<n>We propose a novel training framework in which the model is compelled to acknowledge its ignorance when presented with an intervened question.<n>In practice, we integrate a state-of-the-art model into our framework to validate its effectiveness.
  arXiv  Detail & Related papers  (2025-01-15T12:44:52Z)
• Perceive, Query & Reason: Enhancing Video QA with Question-Guided Temporal Queries [50.47265863322891]
  Video Question Answering (Video QA) is a challenging video understanding task that requires models to comprehend entire videos.<n>Recent advancements in Multimodal Large Language Models (MLLMs) have transformed video QA by leveraging their exceptional commonsense reasoning capabilities.<n>We propose T-Former, a novel temporal modeling method that creates a question-guided temporal bridge between frame-wise visual perception and the reasoning capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-12-26T17:53:14Z)
• MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering [3.117448929160824]
  temporal relations and answering time-sensitive questions is a challenging task for question-answering systems powered by large language models (LLMs)<n>We introduce the TempRAGEval benchmark, which repurposes existing datasets by incorporating temporal perturbations and gold evidence labels.<n>On TempRAGEval, MRAG significantly outperforms baseline retrievers in retrieval performance, leading to further improvements in final answer accuracy.
  arXiv  Detail & Related papers  (2024-12-20T03:58:27Z)
• Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability [19.722009684115434]
  Retrieve-augmented generation (RAG) frameworks have emerged as a promising solution to multi-hop question answering(QA) tasks.<n>Existing RAG frameworks, which usually follows the retrieve-then-read paradigm, often struggle with multi-hop QA with temporal information.<n>This paper proposes a novel framework called review-then-refine, which aims to enhance LLM performance in multi-hop QA scenarios with temporal information.
  arXiv  Detail & Related papers  (2024-12-19T17:48:23Z)
• Do Language Models Understand Time? [2.290956583394892]
  Large language models (LLMs) have revolutionized video-based computer vision applications, including action recognition, anomaly detection, and summarization.<n>This work critically examines the role of LLMs in video processing, with a specific focus on their temporal reasoning capabilities.<n>We analyze challenges posed by existing video datasets, including biases, lack of temporal annotations, and domain-specific limitations that constrain the temporal understanding of LLMs.
  arXiv  Detail & Related papers  (2024-12-18T13:38:06Z)
• STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training [87.58996020705258]
  Video Large Language Models (Video-LLMs) have recently shown strong derivation in basic video understanding tasks.<n>Video-LLMs struggle with compositional reasoning that requires multi-step explicit-temporal inference across object relations, interactions and events.<n>We propose STEP, a novel graph-guided self-training method that enables VideoLLMs to generate reasoning-rich finetuning data from any raw videos to improve itself.
  arXiv  Detail & Related papers  (2024-11-29T11:54:55Z)
• Multi-granularity Contrastive Cross-modal Collaborative Generation for End-to-End Long-term Video Question Answering [53.39158264785098]
  Long-term Video Question Answering (VideoQA) is a challenging vision-and-language bridging task. We present an entirely end-to-end solution for VideoQA: Multi-granularity Contrastive cross-modal collaborative Generation model.
  arXiv  Detail & Related papers  (2024-10-12T06:21:58Z)
• Causal Understanding For Video Question Answering [2.749898166276854]
  Video Question Answering is a challenging task, which requires the model to reason over multiple frames and understand the interaction between different objects to answer questions based on the context provided within the video. Previous approaches leverage either sub-sampled information or causal intervention techniques along with complete video features to tackle the NExT-QA task. In this work we elicit the limitations of these approaches and propose solutions along four novel directions of improvements on the NExT-QA dataset.
  arXiv  Detail & Related papers  (2024-07-23T06:32:46Z)
• RTQ: Rethinking Video-language Understanding Based on Image-text Model [55.278942477715084]
  Video-language understanding presents unique challenges due to the inclusion of highly complex semantic details. We propose a novel framework called RTQ, which addresses these challenges simultaneously. Our model demonstrates outstanding performance even in the absence of video-language pre-training.
  arXiv  Detail & Related papers  (2023-12-01T04:51:01Z)

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.