Summary of Deal: Disentangle and Localize Concept-level Explanations For Vlms, by Tang Li et al.
DEAL: Disentangle and Localize Concept-level Explanations for VLMs
by Tang Li, Mengmeng Ma, Xi Peng
First submitted to arxiv on: 19 Jul 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 Large pre-trained Vision-Language Models (VLMs) are widely used as foundational components for various applications. However, our study reveals that these models struggle to identify fine-grained concepts. Specifically, their explanations of fine-grained concepts are entangled and mislocalized. To address this issue, we propose DEAL, a method that disentangles and localizes concept-level explanations without human annotations. Our approach encourages distinct concept-level explanations while maintaining consistency with category-level explanations. We conduct extensive experiments on various benchmark datasets and vision-language models, showing significant improvements in concept-level explanation disentanglability and localizability. Interestingly, the improved explainability reduces the model’s reliance on spurious correlations, leading to better prediction accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a special kind of computer program that can understand both pictures and words. These programs are called Vision-Language Models or VLMs. They’re really good at doing things like recognizing objects in photos or answering questions about what’s happening in a video. But sometimes, they struggle to understand very specific details within those images or videos. Our research aims to make these models better by helping them explain why they make certain decisions. We developed a new method called DEAL that makes the model’s explanations clearer and more accurate without needing any human help. By improving how well the model explains itself, we can also improve its overall performance and accuracy. |
