Papers › Language Repository for Long Video Understanding
Language Repository for Long Video Understanding
Kumara Kahatapitiya, Kanchana Ranasinghe, Jongwoo Park, Michael S. Ryoo
Language has become a prominent modality in computer vision with the rise of LLMs. Despite supporting long context-lengths, their effectiveness in handling long-term information gradually declines with input length. This becomes critical, especially in applications such as long-form video understanding. In this paper, we introduce a Language Repository (LangRepo) for LLMs, that maintains concise and structured information as an interpretable (i.e., all-textual) representation. Our repository is updated iteratively based on multi-scale video chunks. We introduce write and read operations that focus on pruning redundancies in text, and extracting information at various temporal scales. The proposed framework is evaluated on zero-shot visual question-answering benchmarks including EgoSchema, NExT-QA, IntentQA and NExT-GQA, showing state-of-the-art performance at its scale. Our code is available at https://github.com/kkahatapitiya/LangRepo.
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Code
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Code Syntology ran Syntology
9 samples harvested; 9 ran; 1 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Harvested from kkahatapitiya/langrepo. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero-Shot Video Question Answer | EgoSchema (fullset) | LangRepo (12B) | Accuracy | 41.2 | #20 of 29 | Archive leaderboard | report |
| Zero-Shot Video Question Answer | EgoSchema (subset) | LangRepo (12B) | Accuracy | 66.2 | #3 of 14 | Archive leaderboard | report |
| Zero-Shot Video Question Answer | IntentQA | LangRepo (12B) | Accuracy | 59.1 | #10 of 13 | Archive leaderboard | report |
| Zero-Shot Video Question Answer | NExT-GQA | LangRepo (12B) | Acc@GQA | 17.1 | #7 of 9 | Archive leaderboard | report |
| Zero-Shot Video Question Answer | NExT-QA | LangRepo (12B) | Accuracy | 60.9 | #22 of 27 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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