Semi-Supervised Video Object Segmentation
94 papers with code • 15 benchmarks • 13 datasets
The semi-supervised scenario assumes the user inputs a full mask of the object(s) of interest in the first frame of a video sequence. Methods have to produce the segmentation mask for that object(s) in the subsequent frames.
Benchmarks
These leaderboards are used to track progress in Semi-Supervised Video Object Segmentation
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Libraries
Use these libraries to find Semi-Supervised Video Object Segmentation models and implementationsDatasets
DAVIS
DAVIS 2017
DAVIS 2016
YouTube-VOS 2018
Referring Expressions for DAVIS 2016 & 2017
VOTChallenge
MOSE
BURST
BL30K
VOT2020
Most implemented papers
Accelerating Video Object Segmentation with Compressed Video
We propose an efficient plug-and-play acceleration framework for semi-supervised video object segmentation by exploiting the temporal redundancies in videos presented by the compressed bitstream.
Scalable Video Object Segmentation with Identification Mechanism
This paper delves into the challenges of achieving scalable and effective multi-object modeling for semi-supervised Video Object Segmentation (VOS).
Decoupling Features in Hierarchical Propagation for Video Object Segmentation
z-x-yang/AOT
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To solve such a problem and further facilitate the learning of visual embeddings, this paper proposes a Decoupling Features in Hierarchical Propagation (DeAOT) approach.
Video Object Segmentation in Panoptic Wild Scenes
Considering the challenges in panoptic VOS, we propose a strong baseline method named panoptic object association with transformers (PAOT), which uses panoptic identification to associate objects with a pyramid architecture on multiple scales.
SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos.
Efficient Video Object Segmentation via Network Modulation
linjieyangsc/video_seg
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CVPR 2018
Video object segmentation targets at segmenting a specific object throughout a video sequence, given only an annotated first frame.
A Generative Appearance Model for End-to-end Video Object Segmentation
joakimjohnander/agame-vos
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CVPR 2019
One of the fundamental challenges in video object segmentation is to find an effective representation of the target and background appearance.
RVOS: End-to-End Recurrent Network for Video Object Segmentation
Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequence.
Learning Correspondence from the Cycle-Consistency of Time
xiaolonw/TimeCycle
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CVPR 2019
We introduce a self-supervised method for learning visual correspondence from unlabeled video.
BubbleNets: Learning to Select the Guidance Frame in Video Object Segmentation by Deep Sorting Frames
griffbr/BubbleNets
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CVPR 2019
Semi-supervised video object segmentation has made significant progress on real and challenging videos in recent years.
