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cs.AI2026
Toward Calibrated Mixture-of-Experts Under Distribution Shift
Gina Wong, Drew Prinster, Suchi Saria +2
Calibration aligns a model's predictive uncertainty with the frequencies of its empirical outcomes and is important for understanding and trusting reported probabilities. Recent wo…
cs.CV2026
AttriBE: Quantifying Attribute Expressivity in Body Embeddings for Recognition and Identification
Basudha Pal, Siyuan Huang, Anirudh Nanduri +2
Person re-identification (ReID) systems that match individuals across images or video frames are essential in many real-world applications. However, existing methods are often infl…
cs.CV2026
DiffInf: Influence-Guided Diffusion for Supervision Alignment in Facial Attribute Learning
Basudha Pal, Zhaoyang Wang, Rama Chellappa
Facial attribute classification relies on large-scale annotated datasets in which many traits, such as age and expression, are inherently ambiguous and continuous but are discretiz…