The article discusses how transformer models, originally designed for natural language processing (NLP), have been adapted for computer vision tasks. The key idea behind this adaptation is to use the transformer architecture to process and learn from image inputs. The article explores two fundamental architectures that enabled transformers to excel in computer vision tasks: the Vision Transformer and the Masked Autoencoder Vision Transformer.
The Vision Transformer takes image inputs and processes them by dividing the image into patches, flattening them into vectors, and passing them through an encoder. The architecture maintains spatial information by adding positional embeddings. The Masked Autoencoder Vision Transformer involves an encoder and a decoder that are pre-trained using masking to predict missing patches in images, resulting in significant improvements over the base vision transformer model.
The results show that vision transformers may not outperform CNN-based models for small datasets but can approach or outperform them for larger datasets while requiring less computational resources. Self-supervised learning by masking patches in input images has shown improvements in accuracy, although supervised pre-training still outperforms it. The article also discusses the hybrid architecture that combines CNN feature maps with the vision transformer.
Overall, the article provides insights into how transformer models can be applied to computer vision tasks, with examples, results, and references to relevant research papers for further exploration.
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Source link: https://towardsdatascience.com/from-vision-transformers-to-masked-autoencoders-in-5-minutes-cfd2fa1664ac