Summary of Robust Domain Generalization For Multi-modal Object Recognition, by Yuxin Qiao et al.
Robust Domain Generalization for Multi-modal Object Recognition
by Yuxin Qiao, Keqin Li, Junhong Lin, Rong Wei, Chufeng Jiang, Yang Luo, Haoyu Yang
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 The proposed method addresses limitations in existing approaches to multi-label classification with domain generalization, particularly those leveraging vision-language pre-training. By inferring the actual loss, expanding evaluations to larger backbones, and introducing a novel mix-up loss, Mixup-CLIPood enhances class-aware visual fusion. The paper demonstrates superior performance across multiple datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on improving machine learning models for multi-label classification when dealing with different data distributions. Currently, most approaches only work well in vision object recognition and ignore the connection to natural language. Recent advancements have shown that combining computer vision and natural language processing can help machines learn from a wide range of visual-language pairs. However, these advancements still face some challenges. This paper aims to address these challenges by finding ways to overcome limitations in loss function usage, expanding models to work with different backbones, and improving how images and words are combined. The new method, Mixup-CLIPood, shows better performance than other approaches on multiple datasets. |
Keywords
» Artificial intelligence » Classification » Domain generalization » Loss function » Machine learning » Natural language processing
