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One-Shot Video Object Segmentation
Sergi Caelles,Kevis-Kokitsi Maninis,Jordi Pont-Tuset,Laura Leal-Taixé,Daniel Cremers,Luc Van Gool +5 more
TLDR
One-shot video object segmentation (OSVOS) as mentioned in this paper is based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence.Abstract:
This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one-shot). Although all frames are processed independently, the results are temporally coherent and stable. We perform experiments on two annotated video segmentation databases, which show that OSVOS is fast and improves the state of the art by a significant margin (79.8% vs 68.0%).read more
Citations
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Model-agnostic meta-learning for fast adaptation of deep networks
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Fast Online Object Tracking and Segmentation: A Unifying Approach
TL;DR: This method improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task, and operates online, producing class-agnostic object segmentation masks and rotated bounding boxes at 55 frames per second.
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Generalizing from a Few Examples: A Survey on Few-shot Learning
TL;DR: A thorough survey to fully understand Few-shot Learning (FSL), and categorizes FSL methods from three perspectives: data, which uses prior knowledge to augment the supervised experience; model, which used to reduce the size of the hypothesis space; and algorithm, which using prior knowledgeto alter the search for the best hypothesis in the given hypothesis space.
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Generalizing from a Few Examples: A Survey on Few-Shot Learning
TL;DR: A thorough survey to fully understand Few-Shot Learning (FSL), and categorizes FSL methods from three perspectives: data, which uses prior knowledge to augment the supervised experience; model, which used to reduce the size of the hypothesis space; and algorithm, which using prior knowledgeto alter the search for the best hypothesis in the given hypothesis space.
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The 2017 DAVIS Challenge on Video Object Segmentation
Jordi Pont-Tuset,Federico Perazzi,Sergi Caelles,Pablo Arbeláez,Alexander Sorkine-Hornung,Luc Van Gool +5 more
TL;DR: The scope of the benchmark, the main characteristics of the dataset, the evaluation metrics of the competition, and a detailed analysis of the results of the participants to the challenge are described.
References
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