Learning Like Humans: Analogical Concept Learning for Generalized Category Discovery

Abstract

Generalized Category Discovery (GCD) seeks to uncover novel categories in unlabeled data while preserving recognition of known categories, yet prevailing visual-only pipelines and the loose coupling between supervised learning and discovery often yield brittle boundaries on fine-grained, look-alike categories. We introduce the Analogical Textual Concept Generator (ATCG), a plug-and-play module that analogizes from labeled knowledge to new observations, forming textual concepts for unlabeled samples. Fusing these analogical textual concepts with visual features turns discovery into a visual-textual reasoning process, transferring prior knowledge to novel data and sharpening category separation. ATCG attaches to both parametric and clustering style GCD pipelines and requires no changes to their overall design. Across six benchmarks, ATCG consistently improves overall, known-class, and novel-class performance, with the largest gains on fine-grained data. Our code is available at: https://github.com/zhou-9527/AnaLogical-GCD.

Figures (4)

• Figure 1: Illustration of an example of human Analogical Learning mechanism and the proposed Analogical Textual Concept Generator (ATCG).
• Figure 2: Overview of the AL-GCD Framework. The framework consists of two stages: (1) ATCG training, where the ATCG is trained using labeled images and text embeddings to acquire the ability to generate meaningful analogical text embeddings for unlabeled samples; (2) GCD Training, where ATCG generates text embeddings for unlabeled samples, which are fused with visual embeddings through a fusion head to produce fusion embeddings. These embeddings are optimized via contrastive learning.
• Figure 3: Architecture of the Analogical Textual Concept Generator (ATCG), illustrating its input–output tensor structure.
• Figure 4: Ablation study of the $\alpha$ on Stanford Cars.