Summary of Bigr: Harnessing Binary Latent Codes For Image Generation and Improved Visual Representation Capabilities, by Shaozhe Hao et al.
BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities
by Shaozhe Hao, Xuantong Liu, Xianbiao Qi, Shihao Zhao, Bojia Zi, Rong Xiao, Kai Han, Kwan-Yee K. Wong
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary BiGR is a conditional image generation model that uses compact binary latent codes for training. It’s designed to enhance both generation and representation capabilities. Unlike other models, BiGR unifies generation and discrimination within the same framework. It features a binary tokenizer, masked modeling mechanism, and binary transcoder for predicting binary codes. The model also includes an entropy-ordered sampling method for efficient image generation. Experimental results show that BiGR outperforms others in terms of FID-50k and linear-probe accuracy, demonstrating its potential for applications such as image inpainting, editing, interpolation, and enrichment. BiGR’s zero-shot generalization capabilities allow it to perform well across various vision tasks without requiring structural modifications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BiGR is a new way to create images based on codes. It’s good at making pictures that look real and also good at understanding what’s in those pictures. This model helps both of these things happen at the same time, which is unique. BiGR has special tools like binary tokenizers and entropy-ordered sampling to make it work well. It can even make new images based on text! The results show that BiGR is better than other models at making good images. |
Keywords
» Artificial intelligence » Generalization » Image generation » Image inpainting » Tokenizer » Zero shot
