Title: ProCoSA: Probabilistic Concept Learning with Spatial Alignment
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: human–machine interaction，interpretable AI，concept bottleneck models，expectation–maximization

Abstract: Concepts are human-interpretable semantic attributes that provide intervenable intermediate representations in neural networks for vision. However, obtaining concept annotations is costly and often yields missing labels, limiting interpretability. We propose ProCoSA, a probabilistic framework that treats missing concepts as latent variables and jointly infers concept probabilities and task predictions under partial supervision. To improve spatial consistency and reduce pseudo-label bias, we introduce a spatial alignment prior that encourages concept representations to align with salient image regions, yielding calibrated concept posteriors that support downstream prediction. ProCoSA integrates seamlessly into concept-to-task pipelines without relying on a specific bottleneck architecture. Experiments on four representative datasets under low concept supervision show that ProCoSA consistently matches or outperforms existing methods on both concept and task metrics under identical evaluation protocols.

TL;DR: ProCoSA probabilistically infers missing concepts via spatial alignment, producing calibrated concept signals that improve interpretability and downstream accuracy under sparse supervision.