Relational Pattern Consistency improves generalized category discovery by using invariant relational patterns between novel samples and known-class prototypes for bidirectional knowledge transfer.
Category discovery: An open-world perspective
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Three frameworks adapt foundation models for generalized category discovery under domain shifts via disentanglement and prompt tuning, showing gains on synthetic and real multi-domain data.
A frequency-domain modeling approach transfers optical priors to SAR imagery via paired pre-training, enabling state-of-the-art generalized category discovery on SAR data.
citing papers explorer
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Relational Retrieval: Leveraging Known-Novel Interactions for Generalized Category Discovery
Relational Pattern Consistency improves generalized category discovery by using invariant relational patterns between novel samples and known-class prototypes for bidirectional knowledge transfer.
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Generalized Category Discovery under Domain Shifts: From Vision to Vision-Language Models
Three frameworks adapt foundation models for generalized category discovery under domain shifts via disentanglement and prompt tuning, showing gains on synthetic and real multi-domain data.
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Unlocking Optical Prior: Spectrum-Guided Knowledge Transfer for SAR Generalized Category Discovery
A frequency-domain modeling approach transfers optical priors to SAR imagery via paired pre-training, enabling state-of-the-art generalized category discovery on SAR data.