5 citations · 8 across the 5 of their papers we have counts for
5 papers · 1 filter
PEVA-Net: Prompt-Enhanced View Aggregation Network for Zero/Few-Shot Multi-View 3D Shape Recognition
Dongyun Lin, Yi Cheng, Shangbo Mao +2
Large vision-language models have impressively promote the performance of 2D visual recognition under zero/few-shot scenarios. In this paper, we focus on exploiting the large visio…
SCA-PVNet: Self-and-Cross Attention Based Aggregation of Point Cloud and Multi-View for 3D Object Retrieval
Dongyun Lin, Yi Cheng, Aiyuan Guo +2
To address 3D object retrieval, substantial efforts have been made to generate highly discriminative descriptors of 3D objects represented by a single modality, e.g., voxels, point…
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection
Dongyun Lin, Yiqun Li, Shudong Xie +2
One pivot challenge for image anomaly (AD) detection is to learn discriminative information only from normal class training images. Most image reconstruction based AD methods rely…
Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation
Dongyun Lin, Yanpeng Cao, Wenbing Zhu +1
In industrial product quality assessment, it is essential to determine whether a product is defect-free and further analyze the severity of anomality. To this end, accurate defect…
FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision
Ramanpreet Singh Pahwa, Jin Chao, Jestine Paul +7
Regular inspection of rail valves and engines is an important task to ensure the safety and efficiency of railway networks around the globe. Over the past decade, computer vision a…