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CloSeR is a plug‑and‑play framework that enhances Generalized Category Discovery (GCD) by injecting closed‑set relational knowledge from a lightweight teacher model. The teacher is built by fine‑tuning adapters on labeled data while keeping the backbone frozen, preserving pretrained priors. Unified Relational Distillation then transfers both global sample‑to‑prototype and local sample‑to‑sample relations to the GCD task, reducing optimization interference and improving performance across six benchmarks with DINO and DINOv2 backbones.
arXiv:2607. 00620v1 Announce Type: cross Abstract: Generalized Category Discovery (GCD) aims to recognize known classes while autonomously discovering novel ones in open-world settings.
arXiv:2605. 09420v2 Announce Type: replace-cross Abstract: In this study, we tackle Generalized Category Discovery (GCD) via a Relational Retrieval perspective, explicitly coupling labeled and unlabeled data through bidirectional knowledge transfer.
arXiv:2607. 15919v1 Announce Type: cross Abstract: Data-free knowledge distillation transfers the knowledge encoded in a teacher model to a student model without access to the original training data.
arXiv:2602. 17395v2 Announce Type: replace-cross Abstract: Generalized Category Discovery (GCD) aims to identify novel categories in unlabeled data while leveraging a small labeled subset of known classes.
arXiv:2607. 04548v1 Announce Type: cross Abstract: Novel category discovery aims to identify unseen classes from unlabeled data by transferring knowledge from labeled categories, but most existing methods perform discovery in opaque latent feature spaces.