Search Results for author: Wenjia Wang

Found 20 papers, 8 papers with code

An automated approach to extracting positive and negative clinical research results

no code implementations7 Dec 2022 Xuanyu Shi, Shiyao Xie, Wenjia Wang, Ting Chen, Jian Du

Failure is common in clinical trials since the successful failures presented in negative results always indicate the ways that should not be taken.

Soft BPR Loss for Dynamic Hard Negative Sampling in Recommender Systems

no code implementations25 Nov 2022 Kexin Shi, Yun Zhang, BingYi Jing, Wenjia Wang

In recommender systems, leveraging Graph Neural Networks (GNNs) to formulate the bipartite relation between users and items is a promising way.

Recommendation Systems

Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding

no code implementations30 May 2022 Tianyang Hu, Zhili Liu, Fengwei Zhou, Wenjia Wang, Weiran Huang

Contrastive learning, especially Self-Supervised Contrastive Learning (SSCL), has achieved great success in extracting powerful features from unlabeled data, enabling comparable performance to the supervised counterpart.

Contrastive Learning Data Augmentation +2

WegFormer: Transformers for Weakly Supervised Semantic Segmentation

no code implementations16 Mar 2022 Chunmeng Liu, Enze Xie, Wenjia Wang, Wenhai Wang, Guangyao Li, Ping Luo

Although convolutional neural networks (CNNs) have achieved remarkable progress in weakly supervised semantic segmentation (WSSS), the effective receptive field of CNN is insufficient to capture global context information, leading to sub-optimal results.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Differentiable and Scalable Generative Adversarial Models for Data Imputation

no code implementations10 Jan 2022 Yangyang Wu, Jun Wang, Xiaoye Miao, Wenjia Wang, Jianwei Yin

DIM leverages a new masking Sinkhorn divergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model.


Understanding Square Loss in Training Overparametrized Neural Network Classifiers

no code implementations7 Dec 2021 Tianyang Hu, Jun Wang, Wenjia Wang, Zhenguo Li

Comparing to cross-entropy, square loss has comparable generalization error but noticeable advantages in robustness and model calibration.

Segmenting Transparent Object in the Wild with Transformer

2 code implementations21 Jan 2021 Enze Xie, Wenjia Wang, Wenhai Wang, Peize Sun, Hang Xu, Ding Liang, Ping Luo

This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.

Semantic Segmentation Transparent objects

Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly

no code implementations21 Nov 2020 Cheolhei Lee, Jianguo Wu, Wenjia Wang, Xiaowei Yue

Developing machine learning enabled smart manufacturing is promising for composite structures assembly process.

Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network

no code implementations6 Jul 2020 Tianyang Hu, Wenjia Wang, Cong Lin, Guang Cheng

Overparametrized neural networks trained by gradient descent (GD) can provably overfit any training data.

High-Dimensional Non-Parametric Density Estimation in Mixed Smooth Sobolev Spaces

no code implementations5 Jun 2020 Liang Ding, Lu Zou, Wenjia Wang, Shahin Shahrampour, Rui Tuo

Density estimation plays a key role in many tasks in machine learning, statistical inference, and visualization.

Density Estimation

Segmenting Transparent Objects in the Wild

1 code implementation ECCV 2020 Enze Xie, Wenjia Wang, Wenhai Wang, Mingyu Ding, Chunhua Shen, Ping Luo

To address this important problem, this work proposes a large-scale dataset for transparent object segmentation, named Trans10K, consisting of 10, 428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets.

Semantic Segmentation Transparent objects

Uncertainty Quantification for Bayesian Optimization

no code implementations4 Feb 2020 Rui Tuo, Wenjia Wang

Although the outputs of Bayesian optimization are random according to the Gaussian process assumption, quantification of this uncertainty is rarely studied in the literature.


PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation

1 code implementation4 Nov 2019 Jie Zhao, Lei Dai, Mo Zhang, Fei Yu, Meng Li, Hongfeng Li, Wenjia Wang, Li Zhang

The experimental results show that the PGU-net+ has superior accuracy than the previous state-of-the-art methods on cervical nuclei segmentation.

TextSR: Content-Aware Text Super-Resolution Guided by Recognition

1 code implementation16 Sep 2019 Wenjia Wang, Enze Xie, Peize Sun, Wenhai Wang, Lixun Tian, Chunhua Shen, Ping Luo

Nonetheless, most of the previous methods may not work well in recognizing text with low resolution which is often seen in natural scene images.

Scene Text Recognition Super-Resolution

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

6 code implementations ICCV 2019 Wenhai Wang, Enze Xie, Xiaoge Song, Yuhang Zang, Wenjia Wang, Tong Lu, Gang Yu, Chunhua Shen

Recently, some methods have been proposed to tackle arbitrary-shaped text detection, but they rarely take the speed of the entire pipeline into consideration, which may fall short in practical applications. In this paper, we propose an efficient and accurate arbitrary-shaped text detector, termed Pixel Aggregation Network (PAN), which is equipped with a low computational-cost segmentation head and a learnable post-processing.

Scene Text Detection

Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks

2 code implementations19 Apr 2019 Wenjia Wang, Junxuan Chen, Jie Zhao, Ying Chi, Xuansong Xie, Li Zhang, Xian-Sheng Hua

The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0. 947 $\pm$ 0. 044.

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