UATTA adapts pre-trained text-image models at test time without labels by using disagreement in bidirectional retrieval rankings to estimate and mitigate uncertainty for improved person search.
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SRast is a generalist framework using self-supervised decoupling of gene and spatial representations plus flow matching for physically consistent super-resolution of spatial transcriptomics data with strong zero-shot generalization.
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Pretrain-then-Adapt: Uncertainty-Aware Test-Time Adaptation for Text-based Person Search
UATTA adapts pre-trained text-image models at test time without labels by using disagreement in bidirectional retrieval rankings to estimate and mitigate uncertainty for improved person search.
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Towards Universal Spatial Transcriptomics Super-Resolution: A Generalist Physically Consistent Flow Matching Framework
SRast is a generalist framework using self-supervised decoupling of gene and spatial representations plus flow matching for physically consistent super-resolution of spatial transcriptomics data with strong zero-shot generalization.