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Fast & Accurate Online Video Object Segmentation via Tracking Parts
CVPR 2018 | We propose a fast and accurate video object segmentation algorithm that can immediately start the segmentation process after receiving images. We first utilize a part-based tracking method to deal with challenging factors such as large deformation, occlusion and cluttered background. We next construct an efficient region-of-interest segmentation network to generate part masks, with a similarity-based scoring function to refine these object parts and generate final segmentation outputs.
Collaborators: Jingchun Cheng, Yi-Hsuan Tsai, Wei-Chih Hung, Shengjin Wang, Ming-Hsuan Yang
