Summary of Flair: Vlm with Fine-grained Language-informed Image Representations, by Rui Xiao et al.
FLAIR: VLM with Fine-grained Language-informed Image Representations
by Rui Xiao, Sanghwan Kim, Mariana-Iuliana Georgescu, Zeynep Akata, Stephan Alaniz
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed Fine-grained Language-informed Image Representations (FLAIR) model utilizes detailed image descriptions to learn localized image embeddings, introducing text-conditioned attention pooling on top of local image tokens. This approach excels at retrieving detailed image content, achieving state-of-the-art performance on multimodal retrieval benchmarks and a newly introduced fine-grained retrieval task. FLAIR trained on 30M image-text pairs outperforms models trained on billions of pairs in capturing fine-grained visual information, including zero-shot semantic segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to search for specific parts of an image, like a dog’s ear or a car’s wheel, with incredible accuracy. That’s what this paper is about – developing a new way to understand images by matching them with detailed descriptions. The team created a model called FLAIR that uses these descriptions to learn more about the image’s fine details, allowing it to find specific parts of an image quickly and accurately. This breakthrough could have many practical applications in fields like healthcare, education, or even self-driving cars. |
Keywords
» Artificial intelligence » Attention » Semantic segmentation » Zero shot
