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arXiv Computer Vision By Hailong Ning, Hao Wang, Yimeng Wang, Tao Lei, Renwei Dian, Asoke K. Nandi

Progressive Pseudo-Label Optimization for Point-Supervised Change Detection

Read the original on arXiv Computer Vision →

The paper introduces a two-stage framework for point-supervised change detection that leverages SAM2 priors to generate object-aware candidate masks and refines them with a lightweight CNN and uncertainty-aware loss. In the second stage, a teacher‑student self‑training loop with exponential moving average updates continuously improves pseudo‑labels and model performance. Experiments on WHU-CD, LEVIR-CD, and SYSU-CD show the method surpasses prior weakly supervised approaches and competes with fully supervised ones.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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