Papers by Kumara Kahatapitiya
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. |
Language Repository for Long Video Understanding (2025.findings-acl)
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| Challenge: | Language-based learning models (LLMs) support long context-lengths but their effectiveness in handling long-term information gradually declines with input length. |
| Approach: | They propose a Language Repository (LangRepo) that maintains concise and structured information as an interpretable representation. |
| Outcome: | The proposed framework is evaluated on zero-shot visual question-answering benchmarks. |