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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. -
Ref-DAVIS17
Ref-DAVIS17 is an extension of the DAVIS17 dataset, where it enhances the dataset by providing language descriptions for each specific object present in the videos. -
RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Ob...
Referring video object segmentation (RVOS) aims to accurately segment the target object in the video with the guidance of given language expressions. -
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. -
DAVIS 2016 and DAVIS 2017 datasets
The DAVIS 2016 and DAVIS 2017 datasets for video object segmentation. -
DAVIS-2017
The DAVIS-2017 dataset is a benchmark for video object segmentation -
Adversarial Attacks on Video Object Segmentation with Hard Region Discovery
Video object segmentation has been applied to various computer vision tasks, such as video editing, autonomous driving, and human-robot interaction. -
Youtube-Objects
Video object segmentation is challenging due to the factors like rapidly fast motion, cluttered backgrounds, arbitrary object appearance variation and shape deformation. -
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything by Prompt...
The paper introduces an online method, named SAM-PD, that applies SAM to track and segment objects throughout the video. -
Fast video object segmentation by reference-guided mask propagation
The SegTrack v2 dataset is a benchmark for video object segmentation. -
Youtube-VOS: Sequence-to-sequence video object segmentation
The YouTube-VOS dataset is a benchmark for video object segmentation. -
DAVIS 2017: A Benchmark for Video Object Segmentation
The DAVIS dataset is a benchmark for video object segmentation. -
DAVIS and YouTube-VOS datasets
The DAVIS dataset comprises 60, 30 and 30 video sequences for training, validation and testing, respectively. The YouTube-VOS dataset is a larger dataset with 3471 videos for...
