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One-shot Localization and Segmentation of Medical Images with Foundation Models
Recent advances in Vision Transformers (ViT) and Stable Diffusion (SD) models with their ability to capture rich semantic features of the image have been used for image correspondence tasks on natural images.
Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability
A UNet-like architecture is used to generate input to the Sender network which produces a symbolic sentence, and a Receiver network co-generates the segmentation mask based on the sentence.
Variational Encoder-based Reliable Classification
Machine learning models provide statistically impressive results which might be individually unreliable.
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