50 citations · 70 across the 6 of their papers we have counts for
6 papers
NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation
Jianfeng Wang, Daniela Massiceti, Xiaolin Hu +2
Semi-supervised semantic segmentation involves assigning pixel-wise labels to unlabeled images at training time. This is useful in a wide range of real-world applications where col…
CEDNet: A Cascade Encoder-Decoder Network for Dense Prediction
Gang Zhang, Ziyi Li, Chufeng Tang +2
Multi-scale features are essential for dense prediction tasks, such as object detection, instance segmentation, and semantic segmentation. The prevailing methods usually utilize a…
Active Pointly-Supervised Instance Segmentation
Chufeng Tang, Lingxi Xie, Gang Zhang +3
The requirement of expensive annotations is a major burden for training a well-performed instance segmentation model. In this paper, we present an economic active learning setting,…
Bridging the Gap Between Training and Inference of Bayesian Controllable Language Models
Han Liu, Bingning Wang, Ting Yao +3
Large-scale pre-trained language models have achieved great success on natural language generation tasks. However, it is difficult to control the pre-trained language models to gen…
Dynamic Multi-path Neural Network
Yingcheng Su, Shunfeng Zhou, Yichao Wu +7
Although deeper and larger neural networks have achieved better performance, the complex network structure and increasing computational cost cannot meet the demands of many resourc…
Boosting Adversarial Attacks with Momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang +4
Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences. Adversarial attacks serve…