Summary of Rankclip: Ranking-consistent Language-image Pretraining, by Yiming Zhang et al.
RankCLIP: Ranking-Consistent Language-Image Pretraining
by Yiming Zhang, Zhuokai Zhao, Zhaorun Chen, Zhili Feng, Zenghui Ding, Yining Sun
First submitted to arxiv on: 15 Apr 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 Self-supervised contrastive learning models, such as CLIP, have set new benchmarks for vision-language models in many downstream tasks. However, their dependency on rigid one-to-one mappings overlooks the complex and often multifaceted relationships between and within texts and images. RANKCLIP, a novel pretraining method, extends beyond this framework by introducing list-wise learning and ranking consistency, enabling it to capture nuanced relationships. This leads to significant gains in zero-shot classifications over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RANKCLIP is a new way for computers to learn from pictures and words. It’s like a special kind of training that helps machines understand how things are related. Right now, most computer models think about these relationships in a very simple way – just matching one picture with one word. But RANKCLIP thinks differently. It looks at many pictures and words together and tries to figure out how they’re all connected. This makes it really good at guessing what something is even if it’s never seen it before. |
Keywords
» Artificial intelligence » Pretraining » Self supervised » Zero shot
