no code implementations • 17 May 2024 • Ruibo Wang, Song Zhang, Ping Huang, Donghai Zhang, Haoyu Chen
In computer vision and graphics, the accurate reconstruction of road surfaces is pivotal for various applications, especially in autonomous driving.
no code implementations • 24 Mar 2024 • Ruibo Wang, Song Zhang, Ping Huang, Donghai Zhang, Wei Yan
This research aims to extend the Semantic Neural Radiance Fields (Semantic-NeRF) model by focusing solely on semantic output and removing the RGB output component.
no code implementations • 13 Jun 2023 • Haoping Bai, Shancong Mou, Tatiana Likhomanenko, Ramazan Gokberk Cinbis, Oncel Tuzel, Ping Huang, Jiulong Shan, Jianjun Shi, Meng Cao
We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges.
1 code implementation • 19 May 2023 • Kaichao You, Guo Qin, Anchang Bao, Meng Cao, Ping Huang, Jiulong Shan, Mingsheng Long
Subsequently, we propose a novel Tune mode to bridge the gap between Eval mode and Deploy mode.
no code implementations • 27 Mar 2023 • Zhongcan Li, Ping Huang, Chao Wen, Filipe Rodrigues
This paper aims to develop a heterogeneous graph neural network (HetGNN) model, which can address different types of nodes (i. e., heterogeneous nodes), to investigate the train delay evolution on railway networks.
no code implementations • 24 Feb 2023 • Shancong Mou, Xiaoyi Gu, Meng Cao, Haoping Bai, Ping Huang, Jiulong Shan, Jianjun Shi
In this paper, we propose a Robust GAN-inversion (RGI) method with a provable robustness guarantee to achieve image restoration under unknown \textit{gross} corruptions, where a small fraction of pixels are completely corrupted.
1 code implementation • CVPR 2023 • Xuan Zhang, Shiyu Li, Xi Li, Ping Huang, Jiulong Shan, Ting Chen
In this study, we propose an improved model called DeSTSeg, which integrates a pre-trained teacher network, a denoising student encoder-decoder, and a segmentation network into one framework.
Ranked #35 on Anomaly Detection on MVTec AD
no code implementations • 28 Mar 2022 • Shancong Mou, Meng Cao, Haoping Bai, Ping Huang, Jianjun Shi, Jiulong Shan
To combine the best of both worlds, we present an unsupervised patch autoencoder based deep image decomposition (PAEDID) method for defective region segmentation.
no code implementations • 3 Mar 2022 • Shancong Mou, Meng Cao, Zhendong Hong, Ping Huang, Jiulong Shan, Jianjun Shi
Display front-of-screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process.
1 code implementation • ICLR 2022 • Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, Bin Cui
Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort.
no code implementations • 22 Nov 2021 • Haoping Bai, Meng Cao, Ping Huang, Jiulong Shan
On active learning task, our method achieves 97. 0% Top-1 Accuracy on CIFAR10 with 0. 1% annotated data, and 83. 9% Top-1 Accuracy on CIFAR100 with 10% annotated data.
1 code implementation • NeurIPS 2021 • Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, Bin Cui
Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to smooth out the neighborhood over the graph.
no code implementations • NeurIPS 2021 • Haoping Bai, Meng Cao, Ping Huang, Jiulong Shan
While single-shot quantized neural architecture search enjoys flexibility in both model architecture and quantization policy, the combined search space comes with many challenges, including instability when training the weight-sharing supernet and difficulty in navigating the exponentially growing search space.
Hardware Aware Neural Architecture Search Model Optimization +2