SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discovery

2025-10-21

AI Summary

This paper addresses Generalized Category Discovery (GCD), the task of jointly identifying both known and unknown categories in images under partial supervision. Existing approaches suffer from limited generalizability due to reliance on single-level semantics or manually designed hierarchies. To overcome this, we propose SEAL, a Semantic-aware Hierarchical learning framework. Its key contributions are: (1) hierarchical semantic-guided soft contrastive learning, which leverages natural taxonomic relationships to generate informative soft negative samples; and (2) a cross-granularity consistency module that enforces alignment between fine-grained and coarse-grained predictions to improve semantic coherence. SEAL achieves state-of-the-art performance on fine-grained benchmarks—including SSB, Oxford-Pets, and Herbarium19—and demonstrates strong generalization to coarse-grained datasets, validating its robustness across semantic granularities.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Search and Optimization: Learning to Search
• Computer Vision: Object Detection & Categorization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

This paper investigates the problem of Generalized Category Discovery (GCD). Given a partially labelled dataset, GCD aims to categorize all unlabelled images, regardless of whether they belong to known or unknown classes. Existing approaches typically depend on either single-level semantics or manually designed abstract hierarchies, which limit their generalizability and scalability. To address these limitations, we introduce a SEmantic-aware hierArchical Learning framework (SEAL), guided by naturally occurring and easily accessible hierarchical structures. Within SEAL, we propose a Hierarchical Semantic-Guided Soft Contrastive Learning approach that exploits hierarchical similarity to generate informative soft negatives, addressing the limitations of conventional contrastive losses that treat all negatives equally. Furthermore, a Cross-Granularity Consistency (CGC) module is designed to align the predictions from different levels of granularity. SEAL consistently achieves state-of-the-art performance on fine-grained benchmarks, including the SSB benchmark, Oxford-Pet, and the Herbarium19 dataset, and further demonstrates generalization on coarse-grained datasets. Project page: https://visual-ai.github.io/seal/

Problem

• Categorizing unlabeled images from known and unknown classes
• Overcoming limitations of single-level semantics and manual hierarchies
• Improving generalization and scalability in category discovery

Innovation

• Hierarchical semantic-guided soft contrastive learning
• Cross-granularity consistency aligns multi-level predictions
• Natural hierarchical structures guide framework design