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Heng Yan

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2 papers

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AAAI Conference 2026 Conference Paper

MaRS: A Multi-modality Very-high-resolution Remote Sensing Foundation Model with Cross-Granularity Meta-Modality Learning

Ruoyu Yang
Yinhe Liu
Heng Yan
Yiheng Zhou
Yihan Fu
Han Luo
Yanfei Zhong

The multi-modality remote sensing foundation model (MM-RSFM) has made notable progress recently. However, most existing approaches remain limited to medium-resolution, single-modality, restricting their performance in fine-grained downstream applications such as disaster response and urban planning. In this work, MaRS is proposed, a multi-modality very-high-resolution (VHR) remote sensing foundation model designed for cross-modality granularity interpretation of complex scenes. To achieve this, a multi-modality VHR SAR-optical dataset, MaRS-16M, is constructed through large-scale collection and semi-automated processing, comprising over 16 million paired samples. Unlike previous work, MaRS tackles two fundamental challenges in VHR SAR-optical self-supervised learning (SSL) techniques. Cross-granularity contrastive learning (CGCL) is introduced to alleviate alignment inconsistencies caused by imaging differences, and meta-modality attention (MMA) is designed to unify heterogeneous physical characteristics across modalities. Compared to existing remote sensing foundation models (RSFMs) and general vision foundation models (VFMs), MaRS performs better as a pre-trained backbone across nine multi-modality VHR downstream tasks.

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AAAI Conference 2020 Conference Paper

Bursting the Filter Bubble: Fairness-Aware Network Link Prediction

Farzan Masrour
Tyler Wilson
Heng Yan
Pang-Ning Tan
Abdol Esfahanian

Link prediction is an important task in online social networking as it can be used to infer new or previously unknown relationships of a network. However, due to the homophily principle, current algorithms are susceptible to promoting links that may lead to increase segregation of the network—an effect known as ﬁlter bubble. In this study, we examine the ﬁlter bubble problem from the perspective of algorithm fairness and introduce a dyadic-level fairness criterion based on network modularity measure. We show how the criterion can be utilized as a postprocessing step to generate more heterogeneous links in order to overcome the ﬁlter bubble problem. In addition, we also present a novel framework that combines adversarial network representation learning with supervised link prediction to alleviate the ﬁlter bubble problem. Experimental results conducted on several real-world datasets showed the effectiveness of the proposed methods compared to other baseline approaches, which include conventional link prediction and fairness-aware methods for i. i. d data.

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