The What-Where Transformer achieves explicit what-where separation in a ViT-style backbone via concurrent token and attention-map streams, yielding emergent object discovery from attention maps and better weakly-supervised localization.
Transforming auto-encoders
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A structured survey of representation learning methods for retinal OCT image analysis, covering supervised, self-supervised, generative, multimodal, and foundation model approaches along with datasets and open problems.
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What-Where Transformer: A Slot-Centric Visual Backbone for Concurrent Representation and Localization
The What-Where Transformer achieves explicit what-where separation in a ViT-style backbone via concurrent token and attention-map streams, yielding emergent object discovery from attention maps and better weakly-supervised localization.
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Representation learning from OCT images
A structured survey of representation learning methods for retinal OCT image analysis, covering supervised, self-supervised, generative, multimodal, and foundation model approaches along with datasets and open problems.