2 citations · 5 across the 15 of their papers we have counts for
15 papers
MCPO: Modality-Contrastive Preference Optimization for Multimodal Chain-of-Thought Compression
Guangheng Yang, Zhenliang Ni, Zhenkai Wu +4
Recently, multimodal large-scale reasoning models have demonstrated remarkable capabilities in solving complex tasks through long Chains-of-Thought (M-CoT). However, excessively lo…
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…
Tri-path DINO: Feature Complementary Learning for Remote Sensing Multi-Class Change Detection
Kai Zheng, Hang-Cheng Dong, Shoulei Liu +5
In remote sensing imagery, multi-class change detection (MCD) is crucial for fine-grained monitoring, yet it has long been plagued by feature ambiguity in change regions, which obs…
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…
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…
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…
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…
Center-guided Classifier for Semantic Segmentation of Remote Sensing Images
Wei Zhang, Mengting Ma, Yizhen Jiang +4
Compared with natural images, remote sensing images (RSIs) have the unique characteristic. i.e., larger intraclass variance, which makes semantic segmentation for remote sensing im…
CD-Lamba: Boosting Remote Sensing Change Detection via a Cross-Temporal Locally Adaptive State Space Model
Zhenkai Wu, Xiaowen Ma, Rongrong Lian +4
Mamba, with its advantages of global perception and linear complexity, has been widely applied to identify changes of the target regions within the remote sensing (RS) images captu…
A Novel Scene Coupling Semantic Mask Network for Remote Sensing Image Segmentation
Xiaowen Ma, Rongrong Lian, Zhenkai Wu +8
As a common method in the field of computer vision, spatial attention mechanism has been widely used in semantic segmentation of remote sensing images due to its outstanding long-r…
STeInFormer: Spatial-Temporal Interaction Transformer Architecture for Remote Sensing Change Detection
Xiaowen Ma, Zhenkai Wu, Mengting Ma +4
Convolutional neural networks and attention mechanisms have greatly benefited remote sensing change detection (RSCD) because of their outstanding discriminative ability. Existent R…
CDXLSTM: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory
Zhenkai Wu, Xiaowen Ma, Rongrong Lian +2
In complex scenes and varied conditions, effectively integrating spatial-temporal context is crucial for accurately identifying changes. However, current RS-CD methods lack a balan…
Rethinking Remote Sensing Change Detection With A Mask View
Xiaowen Ma, Zhenkai Wu, Rongrong Lian +2
Remote sensing change detection aims to compare two or more images recorded for the same area but taken at different time stamps to quantitatively and qualitatively assess changes…
LOGCAN++: Adaptive Local-global class-aware network for semantic segmentation of remote sensing imagery
Xiaowen Ma, Rongrong Lian, Zhenkai Wu +6
Remote sensing images usually characterized by complex backgrounds, scale and orientation variations, and large intra-class variance. General semantic segmentation methods usually…
LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images
Xiaowen Ma, Mengting Ma, Chenlu Hu +4
Remote sensing images are known of having complex backgrounds, high intra-class variance and large variation of scales, which bring challenge to semantic segmentation. We present L…