Papers Video Question Answering
“Video Question Answering” 태그가 달린 논문 620편 · 필터 해제
Can We Trust Video Hallucination Detectors? VidHalLoc for Evaluating the Evaluators
Video-language models and video agents can produce hallucinations that conflict with spatiotemporal evidence. Existing benchmarks mainly evaluate model hallucinations, and heterogeneous mechanisms make detector reliabili…
Video Question AnsweringVideo CaptioningEgoSIS: From Factorized Visual Ego-Transitions to Motion-Canonical Spatial Evidence for UAV Reasoning
UAV video question answering requires separating camera motion from changes in the scene, but RGB-only multimodal models receive no explicit, stable reference for that separation. We present EgoSIS, a pose-free adapter t…
Video Question AnsweringSpatial ReasoningEM^2Mem: Event-Centric Multimodal Memory for Large Language Models
Multimodal memory offers a scalable interface for long-video question answering, but existing methods often retrieve captions, frames, transcripts, summaries, or graph facts as isolated fragments. Although searchable, su…
Video Question AnsweringPost-Training VLMs for Video Mistake Detection
Human mistakes are inevitable when following instructions, yet they can lead to severe consequences. As such, there has been an increased interest in developing methods for detecting mistakes in videos, with current meth…
Video Question AnsweringVideoGAIA: A Benchmark for General AI Assistants on Agentic Video Understanding
Video understanding is a fundamental task for evaluating the capabilities of multimodal large language models (MLLMs). However, existing leading models have already achieved approximately 90% accuracy on the Video-MME le…
Video Question AnsweringPhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs
Embodied intelligence and world models require video understanding systems to go beyond recognizing objects and actions and develop an understanding of physical regularities. However, despite their strong performance on …
Video Question AnsweringChronoStitch: Training-Free Composition of Visual KV Memories for Long-Horizon Temporal Reasoning
Long-video question answering requires a model to preserve visual evidence over time without repeatedly reprocessing the same video. A practical approach is to store the vision-language model's internal key-value (KV) ca…
Video Question AnsweringConsiSpace: Learning Geometric Consistency Matters for Video Spatial Reasoning
Video spatial reasoning is essential for navigation-oriented perception and long-video question answering, where models must infer spatial relations across long horizons under changing viewpoints. However, existing multi…
Video Question AnsweringReinforcement LearningSpatial ReasoningSearching Videos as Trees: Self-Correcting Agents for Grounded Long Video QA
Grounded long-video question answering (Grounded LVQA) requires answering a question about a long video while localizing the short evidence interval that supports the answer. Recent agentic methods frame this task as mul…
Video Question AnsweringReinforcement LearningWhat Does a Temporal Benchmark Score Measure? Decomposing Channel Use in Video VLM Evaluation
A score on a temporal video question answering benchmark is meant to measure that a model has temporal understanding, but it conflates two questions. 1. The task question: is the question even temporal, does it need seve…
Video Question AnsweringEvidence-Backed Video Question Answering
Current Video Large Language Models (Video LLMs) excel in question answering (QA) but largely operate as black boxes, providing textual answers without verifiable visual grounding. Existing explainability efforts rely on…
Video Question AnsweringObject SegmentationVisual GroundingOverview of the NLPCC 2026 Shared Task 1: Difficulty-Aware Multilingual and Multimodal Medical Instructional Video Understanding Evaluation
Following the CMIVQA, MMI-VQA, and M4IVQA challenges in NLPCC 2023--2025, we introduce the Difficulty-Aware Medical Instructional Video Question Answering (DA-MIVQA) shared task for NLPCC 2026. DA-MIVQA extends previous …
Video Question AnsweringVisual GroundingEventCoT: Event-centric Video Chain-of-thought for Reasoning Temporal Localization
Reasoning temporal localization (RTL) requires a model to generate an answer that itself contains the time interval supporting it, so high-level reasoning and precise temporal grounding must be produced jointly in a sing…
Video Question AnsweringIncentivizing Vision Language Models to Search for Long Video Question Answering
We introduce VSeek, an agentic framework that transforms long-video question answering (LVQA) from a passive, single-pass perception task into a multi-turn retrieval process. VSeek utilizes a natural language-driven sear…
Video Question AnsweringReinforcement LearningA Cost-Aware, Paired Protocol for Auditing Dynamic Tool Synthesis in Agentic Video Question Answering
Agentic Video Question Answering (VideoQA) systems invoke tools during inference, but their tool libraries are fixed, so recurring procedures are rebuilt from primitives on every question. Synthesizing composite tools co…
Video Question AnsweringFrom Accuracy to Visual Dependence: Auditing and Filtering Modality Collapse in Traffic VideoQA
High benchmark accuracy does not guarantee genuine use of visual evidence. We study this problem in traffic accident Video Question Answering (VideoQA), where correct answers should depend on scene-specific visual eviden…
Video Question AnsweringVisual GroundingFast Enough to Act: Spatio-Temporal Visual Token Merging for Low-Latency Robotic VLMs and VLAs
Vision-language models and vision-language action models endow the robot with unprecedented capabilities. However, the input of video and high-resolution images yields a massive number of visual tokens, leading to extrem…
Video Question AnsweringBridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction
Video understanding is a fundamental capability for multimodal intelligence, and recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance on Video Question Answering (VideoQA) benchmarks. Howe…
Video Question AnsweringGraph it first! Enabling Reasoning on Long-form Egocentric Videos through Scene Graphs
Existing multi-modal large language models (MLLMs) face significant challenges in processing long video sequences due to strict input token limitations. As a result, current video understanding approaches, especially in …
Video Question AnsweringEG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence
Recent advances in Video Large Language Models (Video-LLMs) have yielded promising performance on video question answering (VideoQA). Nevertheless, existing benchmarks are predominantly evaluated through answer correctne…
Video Question AnsweringAnswer Generation