Summary of Vidtok: a Versatile and Open-source Video Tokenizer, by Anni Tang et al.
VidTok: A Versatile and Open-Source Video Tokenizer
by Anni Tang, Tianyu He, Junliang Guo, Xinle Cheng, Li Song, Jiang Bian
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces VidTok, a high-performance video tokenizer that outperforms existing approaches in both continuous and discrete tokenizations. The model combines convolutional layers, up/downsampling modules, and Finite Scalar Quantization (FSQ) to address training instability and codebook collapse. VidTok achieves significant improvements over existing methods across multiple metrics, including PSNR, SSIM, LPIPS, and FVD, under standardized evaluation settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VidTok is a new way to represent video content that’s better than what’s already out there. It uses special techniques like convolutional layers and up/downsampling modules to make sure the video is encoded accurately. This helps with things like generating new videos or understanding existing ones. The paper shows that VidTok works really well, beating other methods in lots of ways. |
Keywords
» Artificial intelligence » Quantization » Tokenizer
