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arXiv · 2611.01551

TopoDPO: Dense Preference Alignment for Structure-Sensitive Aerial Referring Segmentation

Huaiyuan Peng·Jisheng Dang·Wencan Zhang·Jingwen Zhao·Hong Peng·Wenjun Ma·Bin Hu·Qi Tian·Tat-Seng Chua
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Abstract

Preference optimization for multimodal segmentation faces a granularity mismatch: dense masks contain spatially heterogeneous preference signals, whereas image-level objectives assign one shared nonlinear weight to all spatial margins. We introduce TopoDPO, a topology-aware dense preference alignment framework for structure-sensitive aerial referring segmentation. Topo-Prior mines informative preference pairs using connected-component discrepancy and macro-corner recall, while the dense objective applies the preference penalty to each local margin before spatial aggregation against a frozen supervised reference. On Topo-RS-Hard, a 4590-instance structural stress set, TopoDPO improves intersection over union (IoU) from 50.91% to 51.24% over image-level direct preference optimization (DPO) and reduces Betti-0 error from 1.34 to 0.90; paired image-cluster bootstrap intervals against image-level DPO exclude zero for all five reported metrics. Matched supervised controls and a complete factorial analysis separate the effects of topology-aware pair mining, localized correction, and dense reference-relative preference weighting. An unfiltered stratification over all 39,244 validation records further shows the clearest structural gains on samples with moderate or severe pre-alignment component-count errors. The code is publicly available at: https://github.com/phy709/TopoDPO.

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Huaiyuan Peng, Jisheng Dang, Wencan Zhang, Jingwen Zhao, Hong Peng, Wenjun Ma, Bin Hu, Qi Tian, Tat-Seng Chua. 2026-11-01. TopoDPO: Dense Preference Alignment for Structure-Sensitive Aerial Referring Segmentation. https://arxiv.org/abs/2611.01551

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