OmniGCD: Abstracting Generalized Category Discovery for Modality Agnosticism
Generalized Category Discovery (GCD) challenges methods to identify known and novel classes using partially labeled data, mirroring human category learning. Unlike prior GCD methods, which operate within a single modality and require dataset-specific fine-tuning, we propose a modality-agnostic GCD approach inspired by the human brain's abstract category formation. Our $\textbf{OmniGCD}$ leverages modality-specific encoders (e.g., vision, audio, text, remote sensing) to process inputs, followed by dimension reduction to construct a $\textbf{GCD latent space}$, which is transformed at test-time into a representation better suited for clustering using a novel synthetically trained Transforme...
| Authors | Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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