TransGCD: Fine-Grained Semantic Transfer for Generalized Category Discovery

Generalized Category Discovery (GCD) aims to uncover novel categories from unlabeled data while preserving recognition of known categories. Recent GCD methods have achieved substantial performance gains by generating pseudo-text representations for unlabeled samples to provide additional semantic guidance. However, these methods typically derive pseudo-text from global visual representations, which are susceptible to background interference and fail to capture fine-grained object attributes. To address these limitations, we propose TransGCD, a fine-grained semantic transfer framework that transfers object-centric visual semantics to textual representations for enhanced pseudo-text generation. Specifically, we introduce two complementary techniques, termed Object-centric Semantic Decomposition (OSD) and Attribute-guided Semantic Injection (ASI). OSD decomposes global visual representations into complementary object-centric semantics, isolating category-relevant cues from background interference. Building on these decomposed semantics, ASI transfers fine-grained visual attributes into textual cues and injects them into the text encoder to enhance pseudo-text representations. By transferring fine-grained visual semantics into the textual space, TransGCD provides more discriminative semantic guidance for recognizing known categories and discovering novel ones. Extensive experiments across multiple GCD benchmarks demonstrate consistent improvements in known, novel, and overall accuracy, with particularly notable gains on fine-grained datasets. e.g., achieving increases of 8.7% on CUB and 6.4% on Stanford Cars. Code is available at redhttps://anonymous.4open.science/r/TransGCD.