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

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, $\textbf{I}$mplicit $\textbf{V}$ideo $\textbf{Q}$uestion $\textbf{A}$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.

💡 Research Summary

This paper introduces a novel and challenging task in the field of Video Question Answering (VideoQA) called Implicit Video Question Answering (I-VQA). Traditional VideoQA models rely on identifying and grounding “explicit visual evidence”—specific temporal segments in a video that directly depict the answer to a question. However, this approach fails when questions target symbolic meanings, underlying intentions, or emotions, where the necessary evidence is not visually explicit but must be inferred from broader contextual cues.

To address this gap, the authors first formally define the I-VQA task. In I-VQA, the explicit visual evidence corresponding to a question is masked or made inaccessible in the input video. The model must then reason about the question using only the remaining “contextual” visual information. To support this task, they construct a new I-VQA dataset by repurposing existing Grounded-VQA datasets (Next-GQA, E.T. Bench, REXTIME). Through a semi-automated pipeline involving commonsense checks and manual verification, they rigorously filter out questions that can be answered without video or through simple description, ensuring the dataset genuinely requires implicit reasoning.

The core technical contribution is the Implicit Reasoning Model (IRM), a novel framework designed to perform reasoning without explicit evidence. Inspired by human cognitive processes where high-level “intent” drives low-level “actions” and subsequent actions verify the intent, IRM employs a dual-clue reasoning strategy. The model consists of two interconnected modules:

1. Action-Intent Module (AIM): This module analyzes the video context to generate textual candidate clues representing “contextual actions” and their underlying “intentions.” To combat issues like hallucination and language bias inherent in text generation, AIM incorporates a visual verification step that checks candidate clues against the actual video content. A relation classifier then evaluates the relevance of each clue to the implicit question, filtering out unhelpful ones to prevent error propagation.
2. Visual Enhancement Module (VEM): This module enhances the original visual representations by attending to the aspects of the video most relevant to the refined textual clues from AIM. The enhanced visual features provide a more focused context for the final reasoning step, typically performed by a Large Language Model (LLM). Notably, these enhanced features can be fed back into the AIM, creating an iterative refinement loop that improves both clue generation and visual understanding.

Extensive experiments demonstrate the effectiveness of IRM. It outperforms powerful multimodal baselines including GPT-4o, OpenAI-o3, and a fine-tuned VideoChat2 model on the proposed I-VQA benchmark. Furthermore, IRM shows strong generalization capabilities, achieving state-of-the-art performance on related tasks that require implicit understanding, such as advertisement persuasion strategy identification (PSA-V dataset) and future event prediction in traffic videos (SUTD-Traffic dataset). The paper highlights that powerful general-purpose models still struggle with implicit reasoning, as evidenced by GPT-4o’s performance near chance level on I-VQA without explicit evidence. This underscores the significance of the proposed task and the effectiveness of the dual-clue reasoning paradigm introduced by IRM, paving the way for more robust and human-like video understanding systems.