Learning Like Humans: Analogical Concept Learning for Generalized Category Discovery

2026-03-20

AI Summary

This work addresses the challenge of ambiguous fine-grained category boundaries in generalized category discovery (GCD), which arises from reliance solely on visual information and the decoupling of supervision from the discovery process. To this end, we propose the Analogical Text Concept Generator (ATCG), which introduces an analogical reasoning mechanism to generate textual concepts for unlabeled samples based on known categories. By fusing these textual concepts with visual features, ATCG reformulates category discovery as a joint vision–language reasoning process, effectively transferring prior knowledge to enhance discriminability. Designed as a plug-and-play module, ATCG is compatible with both parametric and clustering-based GCD methods without requiring modifications to existing pipelines. Extensive experiments across six benchmark datasets demonstrate consistent and significant improvements in overall, known-class, and novel-class performance, with the largest gains observed in fine-grained scenarios.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Generalized Category Discovery
• analogical reasoning
• fine-grained categories
• visual-textual learning
• novel category discovery

Innovation

• Generalized Category Discovery
• Analogical Reasoning
• Visual-Textual Fusion
• Plug-and-Play Module
• Fine-Grained Recognition