Selected work

Representative Papers

Propagate, Then Sharpen: Post-Hoc Refinement of Frozen Node Classifiers

Sep 28, 2026

This study addresses the challenge of optimizing node classification prediction distributions of frozen models using solely graph structure, without access to node features, model parameters, or gradients. To this end, we propose PtS, a post-processing method that decomposes Potts energy into Dirichlet and Gini components and introduces a "propagate-then-sharpen" mechanism. By alternating between anchor-regularized probability propagation and gradient-free mass-conserving sharpening, PtS effectively mitigates deep over-smoothing. Extensive experiments on nine homophilic graph datasets demonstrate that PtS achieves an average accuracy improvement of 1.71% over APPNP, with gains reaching 3.90% under strong noise conditions, significantly alleviating the accuracy degradation associated with deep propagation.

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Weakly Supervised Polar Low Segmentation in Sentinel-1 SAR Imagery

Aug 14, 2026

This study addresses the challenges of missing annotations and ambiguous boundaries in polar low segmentation from SAR imagery by proposing CREST, a weakly supervised framework. The method incorporates a CORE module to encode spatial connectivity priors, combined with constrained region expansion and dynamic self-bootstrapping loss to effectively suppress background noise and refine pseudo-label quality. Experiments on Sentinel-1 data demonstrate that CREST accurately reconstructs cyclone structures and generates multi-level reliability masks. Furthermore, it outperforms state-of-the-art adversarial erasure methods on ultrasound and VOC datasets. These results establish CREST as an efficient solution for complex object segmentation in scenarios lacking pixel-level annotations, offering robust performance across diverse imaging domains through its novel integration of topological priors and adaptive learning mechanisms.

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Recent publications

Latest Papers

Propagate, Then Sharpen: Post-Hoc Refinement of Frozen Node Classifiers

Sep 28, 2026

This study addresses the challenge of optimizing node classification prediction distributions of frozen models using solely graph structure, without access to node features, model parameters, or gradients. To this end, we propose PtS, a post-processing method that decomposes Potts energy into Dirichlet and Gini components and introduces a "propagate-then-sharpen" mechanism. By alternating between anchor-regularized probability propagation and gradient-free mass-conserving sharpening, PtS effectively mitigates deep over-smoothing. Extensive experiments on nine homophilic graph datasets demonstrate that PtS achieves an average accuracy improvement of 1.71% over APPNP, with gains reaching 3.90% under strong noise conditions, significantly alleviating the accuracy degradation associated with deep propagation.

0 citationsRead paper

Weakly Supervised Polar Low Segmentation in Sentinel-1 SAR Imagery

Aug 14, 2026

This study addresses the challenges of missing annotations and ambiguous boundaries in polar low segmentation from SAR imagery by proposing CREST, a weakly supervised framework. The method incorporates a CORE module to encode spatial connectivity priors, combined with constrained region expansion and dynamic self-bootstrapping loss to effectively suppress background noise and refine pseudo-label quality. Experiments on Sentinel-1 data demonstrate that CREST accurately reconstructs cyclone structures and generates multi-level reliability masks. Furthermore, it outperforms state-of-the-art adversarial erasure methods on ultrasound and VOC datasets. These results establish CREST as an efficient solution for complex object segmentation in scenarios lacking pixel-level annotations, offering robust performance across diverse imaging domains through its novel integration of topological priors and adaptive learning mechanisms.

0 citationsRead paper