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.
Xcon: Learning with experts for fine-grained category discovery
3 Pith papers cite this work. Polarity classification is still indexing.
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EAGC mitigates gradient entanglement in GCD by anchoring supervised gradients and adaptively projecting unlabeled ones, boosting existing methods to new state-of-the-art performance.
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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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.
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The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery
EAGC mitigates gradient entanglement in GCD by anchoring supervised gradients and adaptively projecting unlabeled ones, boosting existing methods to new state-of-the-art performance.
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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.