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The 2018 Davis Challenge on Video Object Segmentation
The 2018 davis challenge on video object segmentation -
DAVSOD, DAVIS, FBMS, SegV2, and ViSal
The dataset is used for video salient object detection and segmentation. It contains video saliency detection datasets, including DAVSOD, DAVIS, FBMS, SegV2, and ViSal. -
The 2019 Davis Challenge on VOS: Unsupervised Multi-Object Segmentation
This paper proposes the Davis 2017 validation set for video object segmentation. -
YouTubeVOS
A large-scale video object segmentation benchmark. -
DAVIS 2019
A benchmark for unsupervised multi-object segmentation. -
Video IPMT
Few-Shot Video Object Segmentation (FSVOS) aims to segment objects in a query video with the same category defined by a few annotated support images. -
YouTube-VOS Dataset
The YouTube-VOS dataset is a sequence-to-sequence video object segmentation dataset, with 1000 videos and 1000 frames per video. -
Learning to Recommend Frame for Interactive Video Object Segmentation in the ...
The paper proposes a framework for interactive video object segmentation (VOS) in the wild, where users can choose some frames for annotations iteratively. -
Point-VOS YouTube
The Point-VOS YouTube dataset consists of 4.4K videos with 4.4K objects annotated with points. -
Point-VOS DAVIS
The Point-VOS DAVIS dataset consists of 32K videos with 600 objects annotated with points. -
Point-VOS Kinetics
The Point-VOS Kinetics dataset consists of 23.9K videos with 120K objects annotated with points. -
Point-VOS Oops
The Point-VOS Oops dataset consists of 8.4K videos with 13.1K objects annotated with points. -
DAVIS Dataset
The DAVIS dataset contains 60 training sequences and 30 validation sequences, with high-quality densely-annotated segmentation mask annotation for each frame. -
YouTube-VOS 2018 dataset
The YouTube-VOS 2018 dataset for video object segmentation.