CPF-GCD enforces low-rank compositional structure on vision backbone features via spatial primitive fields so that novel categories emerge as new activation patterns over a shared vocabulary of reusable visual primitives.
Sptnet: An efficient alternative framework for generalized category discovery with spatial prompt tuning
4 Pith papers cite this work. Polarity classification is still indexing.
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PACO provides a hierarchical online decision system with proxy-simulated initial thresholds and adaptive updates from mature prototypes to enable consistent category discovery in streaming sequences.
SECOS enables direct semantic label prediction in open-world semi-supervised learning by aligning representations with external knowledge for novel classes, outperforming prior methods by up to 5.4% even without post-hoc matching.
LAGCD inserts residual linear adapters into each ViT block plus a distribution alignment loss to improve generalized category discovery by increasing model flexibility while reducing bias between seen and novel classes.
citing papers explorer
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Identifying Latent Concepts and Structures for Generalized Category Discovery
CPF-GCD enforces low-rank compositional structure on vision backbone features via spatial primitive fields so that novel categories emerge as new activation patterns over a shared vocabulary of reusable visual primitives.
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PACO: Proxy-Task Alignment and Online Calibration for On-the-Fly Category Discovery
PACO provides a hierarchical online decision system with proxy-simulated initial thresholds and adaptive updates from mature prototypes to enable consistent category discovery in streaming sequences.
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SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning
SECOS enables direct semantic label prediction in open-world semi-supervised learning by aligning representations with external knowledge for novel classes, outperforming prior methods by up to 5.4% even without post-hoc matching.
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Sparsity Hurts: Simple Linear Adapter Can Boost Generalized Category Discovery
LAGCD inserts residual linear adapters into each ViT block plus a distribution alignment loss to improve generalized category discovery by increasing model flexibility while reducing bias between seen and novel classes.