DURA: Dual Stage Unknown Aware Representation Adaptation for Underwater On-the-Fly Category Discovery

On-the-Fly Category Discovery (OCD) aims to recognize known categories and discover novel ones from an unlabeled data stream. In underwater scenarios, fine-grained inter-class similarity and substantial appearance variations make this task particularly challenging. Existing methods either keep offline representations fixed during inference or adapt representations learned mainly from known categories. To address this issue, we propose DURA, a Dual Stage Unknown Aware Representation Adaptation framework for underwater OCD. In the offline training stage, DURA constructs pseudo unknown samples and explicitly separates them from known category prototypes, preparing the representation for novel category discovery. In the online inference stage, DURA dynamically expands the prototype memory and adapts both the encoder and category prototypes using the observed query stream, allowing the model to adjust to emerging category structures. For systematic evaluation, we establish UOCDBench, to our knowledge the first benchmark dedicated to underwater OCD. It combines our newly introduced AquaOCD343, containing 343 categories and 67,651 images, with four existing underwater datasets. Experiments on UOCDBench and standard OCD benchmarks demonstrate that DURA consistently achieves state-of-the-art performance, particularly improving novel category discovery while maintaining strong known category recognition.