Deja Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse
Summary: Deja Vu accelerates ViT-based VideoLM query processing by learning inter-frame computation reuse via ReuseViT. Memory–compute joint compaction converts reduced FLOPs into GPU speedups, achieving up to 2.64× faster embeddings within 2% error. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Jinwoo Hwang (Korea Advanced Institute of Science and Technology)
- 2. Daeun Kim (Korea Advanced Institute of Science and Technology)
- 3. Sangyeop Lee (Korea Advanced Institute of Science and Technology)
- 4. Yoonsung Kim (Korea Advanced Institute of Science and Technology)
- 5. Guseul Heo (Korea Advanced Institute of Science and Technology)
- 6. Hojoon Kim (Korea Advanced Institute of Science and Technology)
- 7. Yunseok Jeong (Korea Advanced Institute of Science and Technology)
- 8. Tadiwos Meaza (Korea Advanced Institute of Science and Technology)
- 9. Eunhyeok Park (Pohang University of Science and Technology)
- 10. Jeongseob Ahn (Korea University)
- 11. Jongse Park (Korea Advanced Institute of Science and Technology)
BibTeX Citation
@article{hwang_vldb25,
title = {{Deja Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse}},
author = {Hwang, Jinwoo and Kim, Daeun and Lee, Sangyeop and Kim, Yoonsung and Heo, Guseul and Kim, Hojoon and Jeong, Yunseok and Meaza, Tadiwos and Park, Eunhyeok and Ahn, Jeongseob and Park, Jongse},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {10},
pages = {3284--3298},
doi = {10.14778/3748191.3748195},
url = {https://doi.org/10.14778/3748191.3748195},
year = {2025}
}
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