Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue Reasoning

2025-06-09

AI Summary

Existing VideoQA methods rely heavily on explicit visual evidence (e.g., relevant temporal segments), rendering them inadequate for questions requiring symbolic reasoning or intent understanding—tasks involving implicit semantics lacking direct visual grounding. To address this, we introduce Implicit Video Question Answering (I-VQA), a novel task emphasizing context-driven reasoning in the absence of explicit visual evidence. Our contributions are threefold: (1) We construct the first I-VQA benchmark dataset; (2) We propose the Implicit Reasoning Model (IRM), a dual-clue framework that decouples action-level and intent-level reasoning via an Action-Intent Module (AIM) and a Vision-Enhanced Module (VEM); (3) IRM achieves state-of-the-art performance on I-VQA, outperforming GPT-4o, OpenAI-o3, and fine-tuned VideoChat2 by 0.76%–4.87%, and demonstrates superior generalization on downstream tasks including ad understanding and traffic prediction.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Video Question Answering (VideoQA) aims to answer natural language questions based on the given video, with prior work primarily focusing on identifying the duration of relevant segments, referred to as explicit visual evidence. However, explicit visual evidence is not always directly available, particularly when questions target symbolic meanings or deeper intentions, leading to significant performance degradation. To fill this gap, we introduce a novel task and dataset, $ extbf{I}$mplicit $ extbf{V}$ideo $ extbf{Q}$uestion $ extbf{A}$nswering (I-VQA), which focuses on answering questions in scenarios where explicit visual evidence is inaccessible. Given an implicit question and its corresponding video, I-VQA requires answering based on the contextual visual cues present within the video. To tackle I-VQA, we propose a novel reasoning framework, IRM (Implicit Reasoning Model), incorporating dual-stream modeling of contextual actions and intent clues as implicit reasoning chains. IRM comprises the Action-Intent Module (AIM) and the Visual Enhancement Module (VEM). AIM deduces and preserves question-related dual clues by generating clue candidates and performing relation deduction. VEM enhances contextual visual representation by leveraging key contextual clues. Extensive experiments validate the effectiveness of our IRM in I-VQA tasks, outperforming GPT-4o, OpenAI-o3, and fine-tuned VideoChat2 by $0.76%$, $1.37%$, and $4.87%$, respectively. Additionally, IRM performs SOTA on similar implicit advertisement understanding and future prediction in traffic-VQA. Datasets and codes are available for double-blind review in anonymous repo: https://github.com/tychen-SJTU/Implicit-VideoQA.

Problem

• Addresses implicit video question answering without visible cues
• Proposes dual-clue reasoning for symbolic meanings and intentions
• Enhances visual context representation for accurate question answering

Innovation

• Dual-stream modeling of contextual actions and intent clues
• Action-Intent Module for clue generation and relation deduction
• Visual Enhancement Module for contextual visual representation