2 citations · 11 across the 29 of their papers we have counts for
7 papers · 1 filter
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…
TinyViM: Frequency Decoupling for Tiny Hybrid Vision Mamba
Xiaowen Ma, Zhenliang Ni, Xinghao Chen
Mamba has shown great potential for computer vision due to its linear complexity in modeling the global context with respect to the input length. However, existing lightweight Mamb…
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…
DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learning
Xiaowen Ma, Jiawei Yang, Rui Che +2
Remote sensing change detection (RSCD) aims to identify the changes of interest in a region by analyzing multi-temporal remote sensing images, and has an outstanding value for loca…
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…
SSA-Seg: Semantic and Spatial Adaptive Pixel-level Classifier for Semantic Segmentation
Xiaowen Ma, Zhenliang Ni, Xinghao Chen
Vanilla pixel-level classifiers for semantic segmentation are based on a certain paradigm, involving the inner product of fixed prototypes obtained from the training set and pixel…