Coherent Loss: A Generic Framework for Stable Video Segmentation

• URL: http://arxiv.org/abs/2010.13085v1
• Date: Sun, 25 Oct 2020 10:48:28 GMT
• Title: Coherent Loss: A Generic Framework for Stable Video Segmentation
• Authors: Mingyang Qian, Yi Fu, Xiao Tan, Yingying Li, Jinqing Qi, Huchuan Lu, Shilei Wen, Errui Ding
• Abstract summary: We investigate how a jittering artifact degrades the visual quality of video segmentation results. We propose a Coherent Loss with a generic framework to enhance the performance of a neural network against jittering artifacts.
• Score: 103.78087255807482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video segmentation approaches are of great importance for numerous vision tasks especially in video manipulation for entertainment. Due to the challenges associated with acquiring high-quality per-frame segmentation annotations and large video datasets with different environments at scale, learning approaches shows overall higher accuracy on test dataset but lack strict temporal constraints to self-correct jittering artifacts in most practical applications. We investigate how this jittering artifact degrades the visual quality of video segmentation results and proposed a metric of temporal stability to numerically evaluate it. In particular, we propose a Coherent Loss with a generic framework to enhance the performance of a neural network against jittering artifacts, which combines with high accuracy and high consistency. Equipped with our method, existing video object/semantic segmentation approaches achieve a significant improvement in term of more satisfactory visual quality on video human dataset, which we provide for further research in this field, and also on DAVIS and Cityscape.

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