2 citations · 2 across the 5 of their papers we have counts for
7 papers
CMAMBADEPTH: Self-supervised Monocular Depth Estimation with Channel Mamba and Hybrid Attention
Xuezhi Xiang, Jiayao Liu, Heqi Xiang +3
Accurate monocular depth estimation serves as a core enabler for single camera scene understanding. However, existing self-supervised monocular depth estimation methods generally s…
tcnerv:dual-domain temporal context modeling for implicit neural video compression
Xuezhi Xiang, Yixin Zhao, Heqi Xiang +2
Video compression aims to minimize reconstruction distor tion under a constrained bit rate. Existing video implicit neural representations (INRs) often decode frames independently,…
PSMP-CLIP: Patch-Prompt SAM and Multi-Semantic Prompting for CLIP-Based Zero-Shot Anomaly Detection
Xuezhi Xiang, Guanghao Wu, Heqi Xiang +4
Zero-shot anomaly detection aims to localize anomalies without target-domain samples. Existing CLIP-based methods suffer from coarse anomaly maps and limited semantic prompts. We p…
LKASeg:Remote-Sensing Image Semantic Segmentation with Large Kernel Attention and Full-Scale Skip Connections
Xuezhi Xiang, Yibo Ning, Lei Zhang +3
Semantic segmentation of remote sensing images is a fundamental task in geospatial research. However, widely used Convolutional Neural Networks (CNNs) and Transformers have notable…
Self-Supervised Scene Flow Estimation with Point-Voxel Fusion and Surface Representation
Xuezhi Xiang, Xi Wang, Lei Zhang +3
Scene flow estimation aims to generate the 3D motion field of points between two consecutive frames of point clouds, which has wide applications in various fields. Existing point-b…
LKA-ReID:Vehicle Re-Identification with Large Kernel Attention
Xuezhi Xiang, Zhushan Ma, Lei Zhang +3
With the rapid development of intelligent transportation systems and the popularity of smart city infrastructure, Vehicle Re-ID technology has become an important research field. T…
Self-supervised Monocular Depth Estimation with Large Kernel Attention
Xuezhi Xiang, Yao Wang, Lei Zhang +3
Self-supervised monocular depth estimation has emerged as a promising approach since it does not rely on labeled training data. Most methods combine convolution and Transformer to…