Intent-QA

The 'Intent-QA' dataset is used to evaluate video question answering systems by assessing their ability to understand and respond to queries based on video content.

Papers using Intent-QA (7)

LeAdQA: LLM-Driven Context-Aware Temporal Grounding for Video Question Answering2025 · 1 citesBuilding a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs2025 · 1 citesIntentvlm: Open-vocabulary Intention Recognition Through Forward-inverse Modeling With Video-language Models2026Progressive Video Condensation with MLLM Agent for Long-form Video Understanding2026BoxTuning: Directly Injecting the Object Box for Multimodal Model Fine-Tuning2026VideoMultiAgents: A Multi-Agent Framework for Video Question Answering2025Language Repository for Long Video Understanding2024