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URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark
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Cited 48 time in Scopus
- Authors
- Issue Date
- 2020-08
- Citation
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.12360 LNCS, pp.208-223
- Abstract
- We propose a unified referring video object segmentation network (URVOS). URVOS takes a video and a referring expression as inputs, and estimates the object masks referred by the given language expression in the whole video frames. Our algorithm addresses the challenging problem by performing language-based object segmentation and mask propagation jointly using a single deep neural network with a proper combination of two attention models. In addition, we construct the first large-scale referring video object segmentation dataset called Refer-Youtube-VOS. We evaluate our model on two benchmark datasets including ours and demonstrate the effectiveness of the proposed approach. The dataset is released at https://github.com/skynbe/Refer-Youtube-VOS.
- ISSN
- 0302-9743
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