Summary of Understanding the Effect Of Using Semantically Meaningful Tokens For Visual Representation Learning, by Neha Kalibhat et al.
Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning
by Neha Kalibhat, Priyatham Kattakinda, Arman Zarei, Nikita Seleznev, Samuel Sharpe, Senthil Kumar, Soheil Feizi
First submitted to arxiv on: 26 May 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 The paper explores the idea of providing semantically meaningful visual tokens to transformer encoders in a vision-language pre-training framework. This is achieved by extracting instance segmentation masks (tangible tokens) and relationships/actions (intangible tokens) using off-the-shelf segmentation and scene-graph models. A vision-side transformer is then pre-trained by incorporating these tokens and aligning the resultant embeddings with caption embeddings from a text-side encoder. The paper introduces additive attention weights to capture structural and semantic relationships among visual tokens, leading to notable improvements in learned representation quality across text-to-image (+47%) and image-to-text retrieval (+44%) tasks on COCO. Additionally, the approach demonstrates advantages on compositionality benchmarks such as ARO (+18%) and Winoground (+10%). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers better at understanding images by giving them more meaningful information to work with. Currently, computer vision models “see” images in small chunks, which can limit their ability to understand the image as a whole. This new approach breaks down images into smaller parts that have meaning and importance, like objects or actions. By using these meaningful pieces of information, the model can learn more about what’s happening in an image and how it relates to words or phrases. The results show that this approach leads to significant improvements in tasks like describing an image based on a sentence, and understanding the relationships between different parts of an image. |
Keywords
» Artificial intelligence » Attention » Encoder » Instance segmentation » Transformer
