126 citations · 185 across the 10 of their papers we have counts for
9 papers · 1 filter
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…
Contextual-based Image Inpainting: Infer, Match, and Translate
Yuhang Song, Chao Yang, Zhe Lin +4
We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to g…
Semantic Segmentation with Reverse Attention
Qin Huang, Chunyang Xia, Chihao Wu +4
Recent development in fully convolutional neural network enables efficient end-to-end learning of semantic segmentation. Traditionally, the convolutional classifiers are taught to…
Object Boundary Guided Semantic Segmentation
Qin Huang, Chunyang Xia, Wenchao Zheng +3
Semantic segmentation is critical to image content understanding and object localization. Recent development in fully-convolutional neural network (FCN) has enabled accurate pixel-…