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README.md

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-license: mit
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+---
+license: mit
+tags:
+- vision
+- vision-language-model
+- contrastive learning
+- self-supervised learning
+---
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+**COSMOS Model**
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+Authors: [Sanghwan Kim](https://kim-sanghwan.github.io/), [Rui Xiao](https://www.eml-munich.de/people/rui-xiao), [Mariana-Iuliana Georgescu](https://lilygeorgescu.github.io/), [Stephan Alaniz](https://www.eml-munich.de/people/stephan-alaniz), [Zeynep Akata](https://www.eml-munich.de/people/zeynep-akata) 
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+COSMOS is introduced in the paper [COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training](https://arxiv.org/abs/2412.01814). COSMOS is trained in self-supervised learning framework with multi-modal augmentation and cross-attention module. It outperforms CLIP-based models trained on larger datasets in visual perception and contextual understanding tasks. COSMOS achieves strong performance in downstream tasks including zero-shot image-text retrieval, classification, and semantic segmentation segmentation.
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