Summary of Semantic Augmentation in Images Using Language, by Sahiti Yerramilli et al.
Semantic Augmentation in Images using Language
by Sahiti Yerramilli, Jayant Sravan Tamarapalli, Tanmay Girish Kulkarni, Jonathan Francis, Eric Nyberg
First submitted to arxiv on: 2 Apr 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 Recent advancements in diffusion models have enabled the generation of photorealistic images based on textual inputs. However, these models are incredibly data-hungry and require very large labeled datasets for supervised learning, often leading to overfitting. To address this issue, we propose a technique to utilize generated images from diffusion models to augment existing datasets. This paper explores various strategies for effective data augmentation to improve the out-of-domain generalization capabilities of deep learning models. Our approach leverages the substantial datasets used to train these diffusion models, which can significantly reduce the need for manual labeling and collection of large datasets. By combining these generated images with traditional data augmentation techniques, we demonstrate improved performance on various benchmarks, including image classification, object detection, and segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where computers can create super-realistic pictures based on text descriptions. That’s what “diffusion models” do! But, to use these models for tasks like recognizing objects or people in pictures, we need a lot of labeled data, which is time-consuming and expensive to collect. Our idea is to use the images generated by these diffusion models to help train other artificial intelligence (AI) models. This way, we can reduce the amount of work needed to create large datasets, making it easier to develop AI that can work well in real-world situations. |
Keywords
* Artificial intelligence * Data augmentation * Deep learning * Diffusion * Domain generalization * Image classification * Object detection * Overfitting * Supervised
