Summary of Contrastive Localized Language-image Pre-training, by Hong-you Chen et al.
Contrastive Localized Language-Image Pre-Training
by Hong-You Chen, Zhengfeng Lai, Haotian Zhang, Xinze Wang, Marcin Eichner, Keen You, Meng Cao, Bowen Zhang, Yinfei Yang, Zhe Gan
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Contrastive Language-Image Pre-training (CLIP) has been widely adopted as the vision backbone of multimodal large language models (MLLMs), enabling image-text representations for various applications. However, CLIP’s reliance on noisy text annotations at image levels may become insufficient for downstream tasks requiring fine-grained vision representations. To improve localization capability, we propose Contrastive Localized Language-Image Pre-training (CLOC) by incorporating region-text contrastive loss and modules. CLOC produces promptable embeddings that can be easily transformed into region representations given spatial hints. We also design a visually-enriched captioning framework to generate region-text pseudo-labels at scale. By scaling up to billions of annotated images, CLOC enables high-quality regional embeddings for image region recognition and retrieval tasks, making it a drop-in replacement for CLIP to enhance MLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves the way computers understand images by combining text and pictures. The current method, called CLIP, is good at recognizing whole scenes but struggles with small details. To solve this problem, the researchers propose a new method that adds more detail to the image understanding. This method is called CLOC. It uses special modules that help computers better recognize parts of an image. The researchers also developed a way to generate labels for images, which helps computers learn from these images. By using lots of labeled images, the new method can recognize small details in pictures, making it useful for tasks like finding specific objects or understanding what’s happening in an image. |
Keywords
» Artificial intelligence » Contrastive loss
