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Found 2 papers, 1 papers with code
FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Models
The rapid development and reduced barriers to entry for Text-to-Image (T2I) models have raised concerns about the biases in their outputs, but existing research lacks a holistic definition and evaluation framework of biases, limiting the enhancement of debiasing techniques.
UniAP: Towards Universal Animal Perception in Vision via Few-shot Learning
Our proposed model takes support images and labels as prompt guidance for a query image.
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