Papers with IntentQA
ReasVQA: Advancing VideoQA with Imperfect Reasoning Process (2025.naacl-long)
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| Challenge: | Existing approaches to VideoQA often fail when complex reasoning or temporal relationships are involved. |
| Approach: | They propose a method that leverages reasoning processes generated by Multimodal Large Language Models to improve VideoQA models. |
| Outcome: | The proposed method improves VideoQA models on three benchmarks. |
Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA (2026.eacl-long)
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| Challenge: | Recent studies leverage large language models (LLMs) in LVQA benchmarks, achieving exceptional performance while relying on vision language models to convert all visual content into natural language. |
| Approach: | They propose a modular and training-free framework that leverages large language models to generate a small subset of informative frames tailored to each question. |
| Outcome: | The proposed framework achieves state-of-the-art performance among similar models across four benchmark LVQA datasets: EgoSchema, NExT-QA, IntentQA, VideoMME. |
VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs (2024.findings-emnlp)
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| Challenge: | Long video understanding presents unique challenges due to the complexity of reasoning over extended timespans. |
| Approach: | They propose a framework VideoINSTA to leverage large language models for video understanding . they propose 'event-based temporalreasoning' and 'content-based spatial reasoning' |
| Outcome: | The proposed model significantly improves state-of-the-art on three long video question-answering benchmarks. |
Question-Instructed Visual Descriptions for Zero-Shot Video Answering (2024.findings-acl)
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| Challenge: | Existing models for video QA rely on complex architectures, expensive pipelines or closed models like GPTs. |
| Approach: | They propose a single instruction-aware open vision-language model to tackle videoQA using frame descriptions. |
| Outcome: | The proposed framework achieves higher performance than current state-of-the-art models on videoQA benchmarks. |
A Simple LLM Framework for Long-Range Video Question-Answering (2024.emnlp-main)
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| Challenge: | a recent study has shown that short video understanding is not trivial due to the need for long-range temporal reasoning capabilities. |
| Approach: | They propose a language-based short- and long-range question-answering framework LLoVi . they propose 'multi-round summarization prompt' that asks the LLM to summarize the captions . |
| Outcome: | The proposed framework outperforms the state-of-the-art on the EgoSchema dataset and to grounded VideoQA. |