Dual Embedding Learning for Video Instance Segmentation

By Qianyu Feng, Zongxin Yang, Peike Li, Yunchao Wei, Yi Yang. 2019

21 citations recorded by the source corpus.

In this paper, we propose a novel framework to generate high-quality segmentation results in a two-stage style, aiming at video instance segmentation task which requires simultaneous detection, segmentation and tracking of instances. To address this multi-task efficiently, we opt to first select high-quality detection proposals in each frame. The categories of the proposals are calibrated with the global context of video. Then, each selected proposal is extended temporally by a bi-directional Instance-Pixel Dual-Tracker (IPDT) which synchronizes the tracking on both instance-level and pixel-level. The instance-level module concentrates on distinguishing the target instance from other objects while the pixel-level module focuses more on the local feature of the instance. Our proposed method achieved a competitive result of mAP 45.0% on the Youtube-VOS dataset, ranking the 3rd in Track 2 of the 2nd Large-scale Video Object Segmentation Challenge.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Occluded Video Instance Segmentation: A Benchmark (2021, background): “Since the release of the YouTube-VIS dataset, video instance segmentation has attracted great attention in the computer vision community, arising a series of algorithms recently [49, 4, 1, 2, 27, 30, 41, 11].”
Consistent Structural Relation Learning for Zero-Shot Segmentation (2020, unknown): “Generalized Zero-shot Semantic Segmentation Semantic segmentation under fully supervised paradigm [34, 35, 36, 37, 38, 39] and domain adaptation scheme [40, 41, 42] are extensively studied.”
Crossover Learning for Fast Online Video Instance Segmentation (2021, unknown): “With the introduction of YouTubeVIS-2019 dataset [68], tremendous progresses [19, 56, 37, 17] have been made in tackling this challenging task.”
SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation (2021, methodology): “Majority of VIS methods [2, 8, 11, 16, 19, 25, 26] follow the two-stage paradigm based on Mask R-CNN.”
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos (2020, methodology): “Methods such as [20,26,52] also rely on object proposals and/or heuristic post-processing to associate objects over time, unlike our end-to-end bottom-up approach.”