Summary of Cross-domain and Cross-dimension Learning For Image-to-graph Transformers, by Alexander H. Berger et al.
Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers
by Alexander H. Berger, Laurin Lux, Suprosanna Shit, Ivan Ezhov, Georgios Kaissis, Martin J. Menten, Daniel Rueckert, Johannes C. Paetzold
First submitted to arxiv on: 11 Mar 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 set of methods enabling cross-domain and cross-dimension learning for image-to-graph transformers in object detection and relationship prediction tasks. This is achieved through regularized edge sampling loss, domain adaptation frameworks, and projection functions that allow using 2D data to train 3D models. The proposed method demonstrates utility in cross-domain and cross-dimension experiments, outperforming standard transfer learning and self-supervised pretraining on challenging benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us learn how to turn images into graphs and find objects in pictures. Usually, we need lots of training data, but sometimes we don’t have enough. This paper shows how to use some existing data to help train models for new, different tasks. The method works by adding special losses, frameworks, and functions that make the model better at learning from fewer examples. It’s useful for things like detecting roads on maps or finding vessels in brain scans. |
Keywords
» Artificial intelligence » Domain adaptation » Object detection » Pretraining » Self supervised » Transfer learning
