Summary of Calico: Part-focused Semantic Co-segmentation with Large Vision-language Models, by Kiet A. Nguyen et al.
CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models
by Kiet A. Nguyen, Adheesh Juvekar, Tianjiao Yu, Muntasir Wahed, Ismini Lourentzou
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper introduces the task of part-focused semantic co-segmentation, which involves identifying and segmenting common and unique objects and parts across multiple images. To address this challenge, the authors propose CALICO, a novel Large Vision-Language Model that can segment and reason over multiple masks across images. CALICO features two key components: a Correspondence Extraction Module that captures semantic-rich information to identify part-level correspondences between objects, and a Correspondence Adaptation Module that embeds this information into the model to facilitate multi-image understanding in a parameter-efficient manner. The authors also curate MixedParts, a comprehensive dataset containing over 2.4 million samples across 44,000 images with diverse object and part categories. Experimental results show that CALICO achieves robust performance in part-focused semantic co-segmentation when finetuned on only 0.3% of its architecture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding objects and parts in multiple photos. It’s like a big puzzle! The authors created a special computer model called CALICO that can help solve this puzzle by looking at the pictures and finding what’s similar or different between them. They also made a big collection of images with lots of different things to find, which they call MixedParts. This helps the computer learn how to do its job better. The results are really good, especially considering it only had to look at a small part of all the pictures to figure out what’s going on. |
Keywords
» Artificial intelligence » Language model » Parameter efficient
