16 citations · 35 across the 6 of their papers we have counts for
9 papers
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…
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…
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…
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection
Siyang Li, Xiangxin Zhu, Qin Huang +2
When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat ins…
A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical Supervision
Chi-Hao Wu, Qin Huang, Siyang Li +1
Being inspired by child's learning experience - taught first and followed by observation and questioning, we investigate a critically supervised learning methodology for object det…