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Information × Registration Number 2119U006533, Article popup.category Препринт Title Semi-supervised feature sharing for efficient video segmentation (AI translated) popup.author Ponomarchuk AntonPonomarchuk Anton popup.publication 01-01-2019 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/1335 popup.publisher Description In robot sensing and automotive driving domains, producing precise semantic segmentation masks for images can help greatly with environment understanding and, as a result, better interaction with it. These tasks usually need to be processed for images with more the 2 object’s classes. Moreover, semantic segmentation should be done for a short period. Almost all approaches that try to solve this task used heavyweight end-to-end deep neural network or external blocks like GRU [14], LSTM[25] or optical flow [1]. In this work, we provide a deep neural network architecture for learning to extract global high-level features and propagate them among the images that describe the same video’s scene, for speeding up image processing. We provide a propagation strategy without any external blocks. We also provide loss function for training such network with the dataset, where the vast number of images don’t have a segmentation mask. popup.nrat_date 2025-11-05 Close