Video QA

QA: Question Answering

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14 papers in the last four weeks, down 48% on the four weeks before. 0.1% of all new papers.

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Latest papers 137

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Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models
May 3, 2026Martin Q. Ma, Willis Guo, Aditya Agrawal +4Active Perception Efficient VLM Inference
Carnegie Mellon University · MIT
SF20K Competition 2025: Summary and findings
May 2, 2026Ridouane Ghermi, Xi Wang, Vicky Kalogeiton +1VLM Evaluation Video Understanding
CurEvo: Curriculum-Guided Self-Evolution for Video Understanding
Apr 29, 2026Guiyi Zeng, Junqing Yu, Yi-Ping Phoebe Chen +3Video QA Curriculum Learning
Huazhong University of Science and Technology Wuhan, China · La Trobe University Melbourne, Australia · Beijing Institute of Computer Technology and Applications Beijing, China
Interactive Episodic Memory with User Feedback
Apr 27, 2026Nikesh Subedi, Loris Bazzani, Ziad Al-HalahEgocentric Video QA Episodic Memory
University of Utah · University of Verona
Don't Pause! Every prediction matters in a streaming video
Apr 27, 2026Dibyadip Chatterjee, Zhanzhong Pang, Fadime Sener +2Streaming Video Understanding Efficient VLM Inference
National University of Singapore · Google Inc.
UpstreamQA: A Modular Framework for Explicit Reasoning on Video Question Answering Tasks
Apr 25, 2026Jason Nguyen, Ameet Rao, Alexander Chang +2Multimodal Large Language Models Video QA
Lincoln North Star High School · The Charter School of Wilmington · Greenwich High School +2
Grounding Video Reasoning in Physical Signals
Apr 23, 2026Alibay Osmanli, Zixu Cheng, Shaogang GongTemporal Video Grounding Video QA
Queen Mary University of London
CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs
Apr 22, 2026Xingcheng Zhou, Hao Guo, Rui Song +5Visual Question Answering Video QA
Technical University of Munich · University of California, Los Angeles
Progressive Online Video Understanding with Evidence-Aligned Timing and Transparent Decisions
Apr 20, 2026Kecheng Zhang, Zongxin Yang, Mingfei Han +6Streaming Video Understanding Efficient VLM Inference
University of Science and Technology of China · Harvard University · Department of CV, MBZUAI +3
When Text Hijacks Vision: Benchmarking and Mitigating Text Overlay-Induced Hallucination in Vision Language Models
Apr 19, 2026Cui Yakun, Xingqun Qi, TianTian Geng +3VLM Evaluation VLM Hallucination
The Hong Kong University of Science and Technology · Hong Kong, China · University of Birmingham
ForeSea: AI Forensic Search with Multi-modal Queries for Video Surveillance
Mar 24, 2026Hyojin Park, Yi Li, Janghoon Cho +8Multimodal RAG Multimodal IR
Qualcomm AI Research, San Diego, CA, USA · Kyunghee University, Gyeonggi, South Korea
Overreliance on AI in Information-seeking from Video Content
Mar 20, 2026Anders Giovanni Møller, Elisa Bassignana, Francesco Pierri +1Video QA Trust in AI
IT University of Copenhagen · Bocconi University · Politecnico di Milano +1
DynaTokens: Controlling Token Dynamics for Continual Video-Language Understanding
Mar 2, 2026Toan Nguyen, Yang Liu, Celso De Melo +1Continual Learning Video QA
School of Computer Science and Engineering, University of New South Wales, Australia · DEVCOM Army Research Laboratory, USA
NeMo: Needle in a Montage for Video-Language Understanding
Sep 29, 2025Zi-Yuan Hu, Shuo Liang, Duo Zheng +10Temporal Video Grounding Video-Language Model Evaluation
The Chinese University of Hong Kong · Phoenix TV · Stanford University +2
TempCore: Are Video QA Benchmarks Temporally Grounded?
Sep 1, 2025Hyunjong Ok, Jaeho LeeVLM Evaluation Video-Language Model Evaluation
POSTECH
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
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

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Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models

Martin Q. Ma, Willis Guo, Aditya Agrawal, Ankit Gupta+3

Carnegie Mellon University · MIT

Active Perception Efficient VLM Inference Video QA VLM Reasoning Temporal Video Understanding Query-Aware Video Frame Selection

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Large vision-language models (VLMs) have advanced multimodal tasks such as video question answering (QA). However, VLMs face the challenge of selecting frames effectively and efficiently, as standard uniform sampling is expensive and performance may plateau. Inspired by active perception theory, which posits that models gain information by acquiring data that differs from their expectations, we introduce Video Active Perception (VAP), a training-free method to enhance long-form video QA using VLMs. Our approach treats keyframe selection as data acquisition in active perception and leverages a lightweight text-conditioned video generation model to represent prior world knowledge. Empirically, VAP achieves state-of-the-art zero-shot results on long-form or reasoning video QA datasets such as EgoSchema, NExT-QA, ActivityNet-QA, IntentQA, and CLEVRER, achieving an increase of up to 5.6 x frame efficiency by frames per question over standard GPT-4o, Gemini 1.5 Pro, and LLaVA-OV. Moreover, VAP shows stronger reasoning abilities than previous methods and effectively selects keyframes relevant to questions. These findings highlight the potential of leveraging active perception to improve the frame effectiveness and efficiency of long-form video QA.

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