# Coherent Loss: A Generic Framework for Stable Video Segmentation Source: [Coherent Loss: A Generic Framework for Stable Video Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:41:59.280Z Authors: Mingyang Qian, Yi Fu, Xiao Tan, Yingying Li, Jinqing Qi, Huchuan Lu, Shilei Wen, Errui Ding Published: 2020-10-25T10:48:28.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()