Hao Cheng, Hao Jiang, Haoyuan Li, Shangzhe Di, Zhelun Yu, Guanghao Zhang, Tao Zhong, Bolin Li, Wanggui He, Fangxun Shu
We lifted 6 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| becomebright/rekv | — | 2 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| CudaCache | Ran | becomebright/rekv/model/attention/rekv_attention.py pointer only (licence: NONE) · get_code("6a22c918328fa7e5") |
| VectorTensor | Ran | becomebright/rekv/model/attention/rekv_attention.py pointer only (licence: NONE) · get_code("e88b9fb9be32e26b") |
| ContextManager | Not yet run | becomebright/rekv/model/attention/rekv_attention.py pointer only (licence: NONE) · get_code("97e2fa43ed175c74") |
| MemoryUnit | Not yet run | becomebright/rekv/model/attention/rekv_attention.py pointer only (licence: NONE) · get_code("5a1d7dcbc940dc65") |
| get_multi_stage_dot_production_attention | Not yet run | becomebright/rekv/model/attention/rekv_attention.py pointer only (licence: NONE) · get_code("fce25f5f4aff13b2") |
| rekv_attention_forward | Not yet run | becomebright/rekv/model/attention/rekv_attention.py pointer only (licence: NONE) · get_code("eb600d8a98496795") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose ReKV, a novel training-free approach that enables efficient streaming video question-answering (StreamingVQA), by seamlessly integrating with existing Video Large Language Models (Video-LLMs). Traditional VideoQA systems struggle with long videos, as they must process entire videos before responding to queries, and repeat this process for each new question. In contrast, our approach analyzes long videos in a streaming manner, allowing for prompt responses as soon as user queries are received. Building on a common Video-LLM, we first incorporate a sliding-window attention mechanism, ensuring that input frames attend to a limited number of preceding frames, thereby reducing computational overhead. To prevent information loss, we store processed video key-value caches (KV-Caches) in RAM and disk, reloading them into GPU memory as needed. Additionally, we introduce a retrieval method that leverages an external retriever or the parameters within Video-LLMs to retrieve only query-relevant KV-Caches, ensuring both efficiency and accuracy in question answering. ReKV enables the separation of video encoding and question-answering across different processes and GPUs, significantly enhancing the efficiency of StreamingVQA. Through comprehensive experimentation, we validate the efficacy and practicality of our approach, which significantly boosts efficiency and enhances applicability over existing VideoQA models.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2503.00540")
get_code_for_paper("2503.00540")
have("2503.00540")
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