Summary of Improving Fine-grained Understanding in Image-text Pre-training, by Ioana Bica et al.
Improving fine-grained understanding in image-text pre-training
by Ioana Bica, Anastasija Ilić, Matthias Bauer, Goker Erdogan, Matko Bošnjak, Christos Kaplanis, Alexey A. Gritsenko, Matthias Minderer, Charles Blundell, Razvan Pascanu, Jovana Mitrović
First submitted to arxiv on: 18 Jan 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 The paper introduces a simple method for pretraining multimodal representations from image-text pairs called SPARse Fine-grained Contrastive Alignment (SPARC). This approach learns to group image patches corresponding to single words in captions, using a sparse similarity metric. The method computes a language-grouped vision embedding as the weighted average of patches and uses a fine-grained sequence-wise loss that only relies on individual samples. SPARC combines this loss with a contrastive loss between global image and text embeddings to learn representations encoding both global and local information. The approach outperforms competing methods on image-level tasks, such as classification, and region-level tasks, including retrieval, object detection, and segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SPARC is a new way to teach computers how to understand pictures and words together. It works by grouping small parts of the picture with words from a sentence that describe it. This helps the computer learn more about both the big picture and the details within. The method does this in a simple and efficient way, which makes it better than other approaches at understanding images and text. |
Keywords
* Artificial intelligence * Alignment * Classification * Contrastive loss * Embedding * Object detection * Pretraining
