papers

Publications (22)

cs.CV2024

Fine-Grained Building Function Recognition from Street-View Images via Geometry-Aware Semi-Supervised Learning

Weijia Li, Jinhua Yu, Dairong Chen +5

In this work, we propose a geometry-aware semi-supervised framework for fine-grained building function recognition, utilizing geometric relationships among multi-source data to enh…

cs.CV2024

Evidential Graph Contrastive Alignment for Source-Free Blending-Target Domain Adaptation

Juepeng Zheng, Yibin Wen, Jinxiao Zhang +2

In this paper, we firstly tackle a more realistic Domain Adaptation (DA) setting: Source-Free Blending-Target Domain Adaptation (SF-BTDA), where we can not access to source domain…

physics.ao-ph2024

Decomposing weather forecasting into advection and convection with neural networks

Mengxuan Chen, Ziqi Yuan, Jinxiao Zhang +2

Operational weather forecasting models have advanced for decades on both the explicit numerical solvers and the empirical physical parameterization schemes. However, the involved h…

cs.CV2026

RS-Prune: Training-Free Data Pruning at High Ratios for Efficient Remote Sensing Diffusion Foundation Models

Fan Wei, Runmin Dong, Yushan Lai +8

Diffusion-based remote sensing (RS) generative foundation models are cruial for downstream tasks. However, these models rely on large amounts of globally representative data, which…

cs.CE2026

Exascale Hybrid Numerical-AI Ensembles for Operational Flood-Season Forecasting in East Asia: 15-km Decadal Hindcasts and 1-km High-Resolution Capability

Mengxuan Chen, Yunpu Xu, Qiuyan Sun +19

Seasonal forecasting of summer rainfall in East Asia remains a grand challenge, as predictability at 3 to 6 month lead times is constrained by the spring predictability barrier, we…

cs.CV2026

ControlRef: Efficient Layout-Guided Multi-Instance Generation via Anchored 4D-RoPE

Yunkai Yang, Yudong Zhang, Xinying Chen +7

Layout-guided multi-instance generation is essential for controllable image synthesis in Multi-Modal Diffusion Transformers (MM-DiTs). However, integrating this capability into uni…