Summary of Pixelbytes: Catching Unified Representation For Multimodal Generation, by Fabien Furfaro
PixelBytes: Catching Unified Representation for Multimodal Generation
by Fabien Furfaro
First submitted to arxiv on: 16 Sep 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 This report presents PixelBytes, an approach for unified multimodal representation learning that integrates text, audio, action-state, and pixelated images (sprites) into a cohesive representation. Building on sequence models like Image Transformers, PixelCNN, and Mamba-Bytes, the authors explore various model architectures, including Recurrent Neural Networks (RNNs), State Space Models (SSMs), and Attention-based models, with a focus on bidirectional processing and their PxBy embedding technique. Experiments were conducted on the PixelBytes Pokemon dataset and an Optimal-Control dataset to evaluate models based on data reduction strategies and autoregressive learning, specifically examining Long Short-Term Memory (LSTM) networks in predictive and autoregressive modes. The results indicate that autoregressive models perform better than predictive models in this context. Additionally, the authors found that diffusion models can be applied to control problems and parallelized generation. PixelBytes aims to contribute to the development of foundation models for multimodal data processing and generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PixelBytes is a new way to learn representations from multiple types of data, like text, images, and sounds. The idea is to combine these different types of data into one representation that can be used for many tasks. The authors tested this approach using Pokémon data and control problems, and found that it worked well. They also showed that some models are better than others at doing certain tasks. This project hopes to help make it easier to work with lots of different kinds of data. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Embedding » Lstm » Representation learning
