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SynthRef: Generation of Synthetic Referring Expressions for Object Segmentation

2021-06-08Code Available1· sign in to hype

Ioannis Kazakos, Carles Ventura, Miriam Bellver, Carina Silberer, Xavier Giro-i-Nieto

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Abstract

Recent advances in deep learning have brought significant progress in visual grounding tasks such as language-guided video object segmentation. However, collecting large datasets for these tasks is expensive in terms of annotation time, which represents a bottleneck. To this end, we propose a novel method, namely SynthRef, for generating synthetic referring expressions for target objects in an image (or video frame), and we also present and disseminate the first large-scale dataset with synthetic referring expressions for video object segmentation. Our experiments demonstrate that by training with our synthetic referring expressions one can improve the ability of a model to generalize across different datasets, without any additional annotation cost. Moreover, our formulation allows its application to any object detection or segmentation dataset.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
DAVIS 2017 (val)RefVOS + SynthRef-YouTube-VISJ&F 1st frame45.3Unverified
Refer-YouTube-VOSRefVOS-Human REsMean IoU39.5Unverified
Refer-YouTube-VOSRefVOS-Synthetic REsMean IoU35Unverified

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