activity
20242026
collaborators

12 papers

cs.CV2026

Masks Can Talk: Extracting Structured Text Information from Single-Modal Images for Remote Sensing Change Detection

Kai Zheng, Hang-Cheng Dong, Jiatong Pan +3

Remote sensing change detection is pivotal for urban monitoring, disaster assessment, and environmental resource management. Yet, unimodal deep learning methods frequently confuse…

cs.CV2026

Tri-path DINO: Feature Complementary Learning for Remote Sensing Multi-Class Change Detection

Kai Zheng, Hang-Cheng Dong, Shoulei Liu +4

In remote sensing imagery, multi class change detection (MCD) is crucial for fine grained monitoring, yet it has long been constrained by complex scene variations and the scarcity…

cs.CV2026

VLM-Pruner: Buffering for Spatial Sparsity in an Efficient VLM Centrifugal Token Pruning Paradigm

Zhenkai Wu, Xiaowen Ma, Zhenliang Ni +4

Vision-language models (VLMs) excel at image understanding tasks, but the large number of visual tokens imposes significant computational costs, hindering deployment on mobile devi…

cs.CV2025

Changes in Gaza: DINOv3-Powered Multi-Class Change Detection for Damage Assessment in Conflict Zones

Kai Zheng, Zhenkai Wu, Fupeng Wei +6

Accurately and swiftly assessing damage from conflicts is crucial for humanitarian aid and regional stability. In conflict zones, damaged zones often share similar architectural st…

cs.LG2025

Ada-MoGE: Adaptive Mixture of Gaussian Expert Model for Time Series Forecasting

Zhenliang Ni, Xiaowen Ma, Zhenkai Wu +3

Multivariate time series forecasts are widely used, such as industrial, transportation and financial forecasts. However, the dominant frequencies in time series may shift with the…

cs.LG2025

Expert Merging: Model Merging with Unsupervised Expert Alignment and Importance-Guided Layer Chunking

Dengming Zhang, Xiaowen Ma, Zhenliang Ni +4

Model merging, which combines multiple domain-specialized experts into a single model, offers a practical path to endow Large Language Models (LLMs) and Multimodal Large Language M…