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content/CSE5519/CSE5519_A3.md

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 # CSE5519 Advances in Computer Vision (Topic A: 2023 - 2024: Semantic Segmentation)
 
+## Segment Anything
+
+[link to the paper](https://arxiv.org/pdf/2304.02643)
+
+### Novelty in Segment Anything
+
+Brute force approach with large scale training data (400x) more
+
+#### Dataset construction
+
+- Model-assisted manual annotation
+- Semi-automatic annotation
+- Automatic annotation (predict mask for 32x32 patches)
+
+> [!TIP]
+>
+> This paper shows a remarkable breakthrough in semantic segmentation with a brute force approach using a large scale training data. The authors use a transformer encoder to get the final segmentation map.
+>
+> I'm really interested in the scalability of the model. Is there any approach to reduce the training data size or the model size with comparable performance via distillation or other techniques?