Title: Gestalt Generalized Category Discovery
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: generalized category discovery, cognitive science, open-world learning

Abstract: Human cognitive science discovers new categories by first grouping percepts under simple organizing principles and only then abstracting them into concepts. Generalized category discovery (GCD) seeks the same ability, yet most pipelines still map discrete tokens directly to category decisions, concentrating on objectives or prototypes while overlooking the relational organization that precedes induction. We present GesGCD, a cognition-inspired paradigm that progressively aligns GCD with human discovery. First, we insert a compact Hyper-Relation Construction stage between the backbone and the classifier so that tokens are organized as groups rather than isolated atoms, enabling evidence to be pooled before any category decision. Second, we inject Gestalt Psychology Calibration by synthesizing memberships that favor proximity, similarity, and continuity, bringing human-like perceptual grouping into the relational stage without extra supervision. These two steps form a simple perception-to-induction bridge that is orthogonal to prevailing objectives and prototype designs, and that preserves efficiency and reproducibility. Across fine-grained and coarse-grained benchmarks, GesGCD improves all-class metrics while offering intuitive visual evidence and more informative representations. We view GesGCD as a step toward closing the structural gap between machine pipelines and human discovery in open worlds.