405 citations · 2.3k across the 116 of their papers we have counts for
11 papers · 2 filters
Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks
Ye Wang, Yueru Chen, Jongmoo Choi +1
This paper reports a visible and thermal drone monitoring system that integrates deep-learning-based detection and tracking modules. The biggest challenge in adopting deep learning…
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation
Ye Wang, Jongmoo Choi, Yueru Chen +5
Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. It becomes tremendously challenging when…
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
Ye Wang, Jongmoo Choi, Yueru Chen +4
One major technique debt in video object segmentation is to label the object masks for training instances. As a result, we propose to prepare inexpensive, yet high quality pseudo g…
An Interpretable Generative Model for Handwritten Digit Image Synthesis
Yao Zhu, Saksham Suri, Pranav Kulkarni +3
An interpretable generative model for handwritten digits synthesis is proposed in this work. Modern image generative models, such as Generative Adversarial Networks (GANs) and Vari…
Unsupervised Multi-modal Neural Machine Translation
Yuanhang Su, Kai Fan, Nguyen Bach +2
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results with only large monolingual corpora in each language. However, the uncertainty of associatin…
Interpretable Convolutional Neural Networks via Feedforward Design
C. -C. Jay Kuo, Min Zhang, Siyang Li +2
The model parameters of convolutional neural networks (CNNs) are determined by backpropagation (BP). In this work, we propose an interpretable feedforward (FF) design without any B…
Defense Against Adversarial Attacks with Saak Transform
Sibo Song, Yueru Chen, Ngai-Man Cheung +1
Deep neural networks (DNNs) are known to be vulnerable to adversarial perturbations, which imposes a serious threat to DNN-based decision systems. In this paper, we propose to appl…
Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention
Chao Yang, Taehwan Kim, Ruizhe Wang +2
Image translation between two domains is a class of problems aiming to learn mapping from an input image in the source domain to an output image in the target domain. It has been a…
SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting
Yuhang Song, Chao Yang, Yeji Shen +3
In this paper, we focus on image inpainting task, aiming at recovering the missing area of an incomplete image given the context information. Recent development in deep generative…
Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart
Chao Yang, Yuhang Song, Xiaofeng Liu +2
Recent advances in deep generative models have shown promising potential in image inpanting, which refers to the task of predicting missing pixel values of an incomplete image usin…
Instance Embedding Transfer to Unsupervised Video Object Segmentation
Siyang Li, Bryan Seybold, Alexey Vorobyov +3
We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks. The instance embedding network…