collaborators

30 papers

cs.CV2026

Topology-Aware Neighborhood Learning for Source-Free Cross-Scene Hyperspectral Image Classification

Qingmei Li, Juepeng Zheng, Jiarui Zhang +2

Domain adaptation has advanced cross-scene hyperspectral image classification, significantly improving discriminative capability in complex scenarios. However, privacy rules or sto…

cs.CV2026

Ground, Cover, and Refine: Evidence-Centric Frame Selection for Long-Video Question Answering

Fan Wei, Siru Zhong, Runmin Dong +3

Long-video question answering requires identifying sparse yet critical evidence from videos containing thousands of frames under a constrained visual-token budget. Existing methods…

cs.CV2026

Attend, Transform, or Silence: Operator-Level Visual Skipping for Efficient Multimodal LLM Inference

Zhaoyang Luo, Runmin Dong, Miao Yang +4

Multimodal large language models (MLLMs) increasingly process long visual-token sequences, increasing the overall inference computation. Existing acceleration methods usually remov…

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

Learning to Balance: Decoupled Siamese Diffusion Transformer for Reference-Based Remote Sensing Image Super-Resolution

Bin Luo, Runmin Dong, Zhaoyang Luo +4

Diffusion-based methods demonstrate significant potential for remote sensing image super-resolution at large scaling factors, particularly in reference-based super-resolution (RefS…

cs.CV2026

CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentation

Shilei Cao, Ziyang Gong, Hehai Lin +10

In Remote Sensing (RS), Parameter-Efficient Fine-Tuning (PEFT) has emerged as a key approach to activate the generalizable representation ability of foundation models for downstrea…