Summary of Your Image Is My Video: Reshaping the Receptive Field Via Image-to-video Differentiable Autoaugmentation and Fusion, by Sofia Casarin et al.
Your Image is My Video: Reshaping the Receptive Field via Image-To-Video Differentiable AutoAugmentation and Fusion
by Sofia Casarin, Cynthia I. Ugwu, Sergio Escalera, Oswald Lanz
First submitted to arxiv on: 22 Mar 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 proposed Differentiable Augmentation Search (DAS) method enables the efficient exploration of large search spaces for image classification and semantic segmentation tasks. By generating variations of images that can be processed as videos, DAS leverages the increased receptive field in the temporal dimension to improve spatial receptive field reshaping. This is achieved by selecting task-dependent transformations guided by DAS. Compared to standard augmentation alternatives, DAS improves accuracy on various datasets, including ImageNet, Cifar10, Cifar100, Tiny-ImageNet, Pascal-VOC-2012, and CityScapes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to use data more efficiently in deep learning models. Instead of making bigger models, they found a way to make the most out of the data you already have. They created a method called DAS that can quickly try many different ways to change images into videos. This helps the model learn better and makes it work better on different tasks like classifying pictures or identifying objects in scenes. |
Keywords
* Artificial intelligence * Deep learning * Image classification * Semantic segmentation
