12 citations · 29 across the 13 of their papers we have counts for
13 papers
Prompt-Driven Lightweight Foundation Model for Instance Segmentation-Based Fault Detection in Freight Trains
Guodong Sun, Qihang Liang, Xingyu Pan +2
Accurate visual fault detection in freight trains remains a critical challenge for intelligent transportation system maintenance, due to complex operational environments, structura…
LaSSM: Efficient Semantic-Spatial Query Decoding via Local Aggregation and State Space Models for 3D Instance Segmentation
Lei Yao, Yi Wang, Yawen Cui +2
Query-based 3D scene instance segmentation from point clouds has attained notable performance. However, existing methods suffer from the query initialization dilemma due to the spa…
GaussianCross: Cross-modal Self-supervised 3D Representation Learning via Gaussian Splatting
Lei Yao, Yi Wang, Yi Zhang +2
The significance of informative and robust point representations has been widely acknowledged for 3D scene understanding. Despite existing self-supervised pre-training counterparts…
SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation
Lei Yao, Yi Wang, Moyun Liu +1
In recent years, transformer-based models have exhibited considerable potential in point cloud instance segmentation. Despite the promising performance achieved by existing methods…
Multiple Prior Representation Learning for Self-Supervised Monocular Depth Estimation via Hybrid Transformer
Guodong Sun, Junjie Liu, Mingxuan Liu +2
Self-supervised monocular depth estimation aims to infer depth information without relying on labeled data. However, the lack of labeled information poses a significant challenge t…
Efficient Visual Fault Detection for Freight Train via Neural Architecture Search with Data Volume Robustness
Yang Zhang, Mingying Li, Huilin Pan +2
Deep learning-based fault detection methods have achieved significant success. In visual fault detection of freight trains, there exists a large characteristic difference between i…
A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving
Moyun Liu, Bing Chen, Youping Chen +4
Depth completion is a crucial task in autonomous driving, aiming to convert a sparse depth map into a dense depth prediction. Due to its potentially rich semantic information, RGB…
A Stronger Stitching Algorithm for Fisheye Images based on Deblurring and Registration
Jing Hao, Jingming Xie, Jinyuan Zhang +1
Fisheye lens, which is suitable for panoramic imaging, has the prominent advantage of a large field of view and low cost. However, the fisheye image has a severe geometric distorti…
LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image
Moyun Liu, Youping Chen, Lei He +2
X-ray image plays an important role in manufacturing industry for quality assurance, because it can reflect the internal condition of weld region. However, the shape and scale of d…
Adaptive Fusion Affinity Graph with Noise-free Online Low-rank Representation for Natural Image Segmentation
Yang Zhang, Moyun Liu, Huiming Zhang +2
Affinity graph-based segmentation methods have become a major trend in computer vision. The performance of these methods relies on the constructed affinity graph, with particular e…
A Lightweight and Accurate Recognition Framework for Signs of X-ray Weld Images
Moyun Liu, Jingming Xie, Jing Hao +3
X-ray images are commonly used to ensure the security of devices in quality inspection industry. The recognition of signs printed on X-ray weld images plays an essential role in di…
A Unified Light Framework for Real-time Fault Detection of Freight Train Images
Yang Zhang, Moyun Liu, Yang Yang +2
Real-time fault detection for freight trains plays a vital role in guaranteeing the security and optimal operation of railway transportation under stringent resource requirements.…
Affinity Fusion Graph-based Framework for Natural Image Segmentation
Yang Zhang, Moyun Liu, Jingwu He +2
This paper proposes an affinity fusion graph framework to effectively connect different graphs with highly discriminating power and nonlinearity for natural image segmentation. The…