# Fanziyang-v/FlashVID

<https://repo.masters-masters.com/en/repos/Fanziyang-v/FlashVID>

[Home](https://repo.masters-masters.com/en) › [Fanziyang-v](https://repo.masters-masters.com/en/owners/Fanziyang-v) › [FlashVID](https://repo.masters-masters.com/en/repos/Fanziyang-v/FlashVID)

Fanziyang-v/FlashVID has 125 stars on GitHub. Its RepoMasters card: Prototype tier. \[ICLR 2026 Oral\] FlashVID: Efficient Video Large Language Models via…

| Field | Value |
| --- | --- |
| Owner | [Fanziyang-v](https://repo.masters-masters.com/en/owners/Fanziyang-v) |
| Stars | 125 |
| Forks | 6 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | MIT |
| Created | 2026 |
| Topics | efficiency, multimodal, token-compression, video-llms, flashvid |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [Fanziyang-v/FlashVID](https://github.com/Fanziyang-v/FlashVID) |

## Repository

\[ICLR 2026 Oral\] FlashVID: Efficient Video Large Language Models via Training-free Tree-based Spatiotemporal Token Merging

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.