Summary of Caption-driven Explorations: Aligning Image and Text Embeddings Through Human-inspired Foveated Vision, by Dario Zanca et al.
Caption-Driven Explorations: Aligning Image and Text Embeddings through Human-Inspired Foveated Vision
by Dario Zanca, Andrea Zugarini, Simon Dietz, Thomas R. Altstidl, Mark A. Turban Ndjeuha, Leo Schwinn, Bjoern Eskofier
First submitted to arxiv on: 19 Aug 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 paper introduces a new dataset, CapMIT1003, which contains captions and click-contingent image explorations to study human attention during the captioning task. The authors also propose a zero-shot method, NevaClip, that combines CLIP models with NeVA algorithms to predict visual scanpaths. NevaClip generates fixations to align representations of foveated visual stimuli and captions, outperforming existing human attention models in plausibility for captioning and free-viewing tasks. This research advances our understanding of human attention and improves scanpath prediction models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how people look at pictures when trying to describe what’s happening in them. They created a big collection of images with captions, and then looked at how people explored the pictures as they wrote their descriptions. The authors also made a new way to predict where people will look next based on words and pictures. This new method works better than old methods for tasks like captioning and just looking at pictures. |
Keywords
* Artificial intelligence * Attention * Zero shot
