8 citations · 27 across the 11 of their papers we have counts for
9 papers · 1 filter
Synergistic Network Learning and Label Correction for Noise-robust Image Classification
Chen Gong, Kong Bin, Eric J. Seibel +3
Large training datasets almost always contain examples with inaccurate or incorrect labels. Deep Neural Networks (DNNs) tend to overfit training label noise, resulting in poorer mo…
Stochastic Actor-Executor-Critic for Image-to-Image Translation
Ziwei Luo, Jing Hu, Xin Wang +5
Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces. In t…
Transferable Adversarial Examples for Anchor Free Object Detection
Quanyu Liao, Xin Wang, Bin Kong +5
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result. The vulnerability has led to a sur…
Fast Local Attack: Generating Local Adversarial Examples for Object Detectors
Quanyu Liao, Xin Wang, Bin Kong +4
The deep neural network is vulnerable to adversarial examples. Adding imperceptible adversarial perturbations to images is enough to make them fail. Most existing research focuses…
Category-wise Attack: Transferable Adversarial Examples for Anchor Free Object Detection
Quanyu Liao, Xin Wang, Bin Kong +4
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbations can completely change the classification results. Their vulnerability has…
Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting
Xian Zhang, Xin Wang, Bin Kong +6
Prior knowledge of face shape and structure plays an important role in face inpainting. However, traditional face inpainting methods mainly focus on the generated image resolution…
DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction
Zhihui Guo, Junjie Bai, Yi Lu +5
A novel centerline extraction framework is reported which combines an end-to-end trainable multi-task fully convolutional network (FCN) with a minimal path extractor. The FCN simul…
Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding
Bin Kong, Xin Wang, Junjie Bai +7
Modeling the sequential information of image sequences has been a vital step of various vision tasks and convolutional long short-term memory (ConvLSTM) has demonstrated its superb…
Residual Attention based Network for Hand Bone Age Assessment
Eric Wu, Bin Kong, Xin Wang +8
Computerized automatic methods have been employed to boost the productivity as well as objectiveness of hand bone age assessment. These approaches make predictions according to the…