Summary of X-former: Unifying Contrastive and Reconstruction Learning For Mllms, by Sirnam Swetha et al.
X-Former: Unifying Contrastive and Reconstruction Learning for MLLMs
by Sirnam Swetha, Jinyu Yang, Tal Neiman, Mamshad Nayeem Rizve, Son Tran, Benjamin Yao, Trishul Chilimbi, Mubarak Shah
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM)
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 This paper focuses on enhancing the visual representations of Multimodal Large Language Models (MLLMs) by combining high-frequency and detailed visual representations with semantically-enriched low-frequency representations. The proposed approach, X-Former, is a lightweight transformer module that exploits the strengths of contrastive learning (CL) and masked image modeling (MIM). It involves bootstrapping vision-language representation learning from frozen vision encoders, CLIP-ViT and MAE-ViT, and then bootstraps vision-to-language generative learning from a frozen LLM. The approach is evaluated on tasks demanding detailed visual understanding, including the GQA dataset, and shows superior capabilities in visual reasoning and fine-grained visual perception. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes big advancements in how computers understand pictures and words together. Right now, computers are really good at understanding general things about pictures, but they struggle to understand tiny details. To fix this, the researchers created a new way to combine two different ways of looking at pictures: one that learns from matching pictures with words, and another that learns by filling in missing parts of pictures. They called their new way “X-Former” and tested it on lots of picture-related tasks. The results show that X-Former is really good at understanding pictures and can even do things like recognize specific objects or understand what’s happening in a scene. |
Keywords
* Artificial intelligence * Bootstrapping * Mae * Representation learning * Transformer * Vit
