提出层次语义学习框架，提升未知类别图像的发现能力。
SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discovery
- 利用自然层级结构生成软负样本，改进对比学习
- 在细粒度数据集上达最新性能，跨数据集泛化强
- 适合需要发现未知类别的图像分类场景
本文研究广义类别发现（GCD）问题。给定部分标注数据集，GCD旨在对所有未标注图像进行分类，无论其属于已知或未知类别。现有方法通常依赖单一层次语义或人工设计的抽象层级，限制了泛化性和可扩展性。为此，我们提出语义感知层次学习框架SEAL，基于自然且易获取的层级结构。SEAL采用层次语义引导的软对比学习，利用层级相似性生成有信息量的软负样本，克服传统对比损失对所有负样本同等处理的局限。此外，设计跨粒度一致性（CGC）模块，对不同粒度层级的预测结果进行对齐。SEAL在细粒度基准测试中表现卓越，涵盖SSB、Oxford-Pet和Herbarium19数据集，并在粗粒度数据集上展示良好泛化能力。
原文摘要 · Abstract (English)
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/
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