A Tri-Branch Network with Prototype-aware Matching for Universal Category Discovery

In this paper, we propose a novel task, Universal Category Discovery (UCD), to address the challenge of partial overlap between source and target domain categories. Different from previous tasks that assume all known categories exist in the target domain, UCD introduces "private-known" categories that only exist in the source domain and aims to classify unlabeled data as "common" or "novel" categories while avoiding misclassifying them into "private-known" categories. For this task, we propose a Tri-branch network with bidirectional Prototype-aware Matching (TriPM). TriPM effectively transfers knowledge from labeled to unlabeled data by bidirectionally matching similar data pairs, while a prototype matching strategy reduces the negative transfer risk from "private-known" categories. Finally, we propose a tri-branch network to decouple knowledge acquisition from labeled data, unlabeled data, and their interactions, which can avoid knowledge forgetting, explore novel patterns, and transfer common knowledge, respectively. Experiments demonstrate our model’s superiority over SOTA methods.