Summary of The Solution For the 5th Gcaiac Zero-shot Referring Expression Comprehension Challenge, by Longfei Huang et al.
The Solution for the 5th GCAIAC Zero-shot Referring Expression Comprehension Challenge
by Longfei Huang, Feng Yu, Zhihao Guan, Zhonghua Wan, Yang Yang
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 This report presents a solution for zero-shot referring expression comprehension, leveraging visual-language multimodal base models like CLIP and SAM. By introducing visual prompts and considering textual prompts, our approach achieves accuracy rates of 84.825% on the A leaderboard and 71.460% on the B leaderboard, securing the first position. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand what someone is talking about in a picture. Zero-shot referring expression comprehension is like that – you try to figure out what’s being referred to without any specific training. Researchers have been working on improving this task using pre-trained models, and our approach does just that. We combine visual prompts with textual ones to predict what’s being referred to. |
Keywords
» Artificial intelligence » Sam » Zero shot
