Causal Reasoning in Videos

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6 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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KARMA-MV: A Benchmark for Causal Question Answering on Music Videos
May 5, 2026Archishman Ghosh, Abhinaba Roy, Dorien HerremansAudio-Visual Understanding Video QA
AMAAI Lab, Singapore University of Technology and Design
ChainReaction: Causal Chain-Guided Reasoning for Modular and Explainable Causal-Why Video Question Answering
Aug 28, 2025Paritosh Parmar, Eric Peh, Basura FernandoVideo QA Multimodal CoT Reasoning
Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore · Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore
TEMPURA: Temporal Event Masked Prediction and Understanding for Reasoning in Action
May 2, 2025Jen-Hao Cheng, Yi-Hao Peng, Huapeng Zhou +11Temporal Action Segmentation Temporal Video Grounding
University of Washington · Carnegie Mellon University · National Yang Ming Chiao Tung University +1
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
Apr 1, 2024Paritosh Parmar, Eric Peh, Ruirui Chen +4Video QA Causal Reasoning
Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore · Nanyang Technological University · National University of Singapore +1

May 5, 2026, cs.CVJ/K move · Enter open · S save

KARMA-MV: A Benchmark for Causal Question Answering on Music Videos

Archishman Ghosh, Abhinaba Roy, Dorien Herremans

AMAAI Lab, Singapore University of Technology and Design

Audio-Visual Understanding Video QA Multimodal QA Audio-Visual QA Causal Reasoning Causal Reasoning in Videos

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While significant progress has been made in Video Question Answering and cross-modal understanding, causal reasoning about how visual dynamics drive musical structure in music videos remains under-explored. We introduce KARMA-MV, a large-scale multiple-choice QA dataset derived from 2,682 YouTube music videos, designed to test models' ability to integrate temporal audio-visual cues and reason about visual-to-musical influence across reasoning, prediction, and counterfactual questions. Unlike traditional datasets requiring manual annotation, KARMA-MV leverages LLM reasoning for scalable generation and validation, yielding 37,737 MCQs. We propose a causal knowledge graph (CKG) approach that augments vision-language models (VLMs) with structured retrieval of cross-modal dependencies. Experiments on state-of-the-art VLMs and LLMs show consistent gains from CKG grounding -- especially for smaller models -- establishing the value of explicit causal structure for music-video reasoning. KARMA-MV provides a new benchmark for advancing causal audio-visual understanding beyond correlation.

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Latest papers 34

KARMA-MV: A Benchmark for Causal Question Answering on Music Videos

ChainReaction: Causal Chain-Guided Reasoning for Modular and Explainable Causal-Why Video Question Answering

TEMPURA: Temporal Event Masked Prediction and Understanding for Reasoning in Action

CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes