태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
The interactive scenario assumes the user gives iterative refinement inputs to the algorithm, in our case in the form of a scribble, to segment the objects of interest. Methods have to produce a segmentation mask for that object in all the frames of a video sequence taking into account all the user interactions.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The Densely Annotation Video Segmentation dataset (DAVIS) is a high quality and high resolution densely annotated video segmentation dataset under two resolutions, 480p and 1080p. There are 50 video sequences with 3455 densely annotated frames in pixel level. 30 videos with 2079 frames are for training and 20 videos with 1376 frames are for validation. Source: TENet: Triple Excitation Network for Video Salient Object Detection
DAVIS17 is a dataset for video object segmentation. It contains a total of 150 videos - 60 for training, 30 for validation, 60 for testing Source: Siam R-CNN: Visual Tracking by Re-Detection Image Source: https://www.researchgate.net/figure/LucidTracker-qualitative-results-on-DAVIS-17-test-dev-set-Frames-sampled-along-the_fig5_331792902
CoMplex video Object SEgmentation (MOSE) is a dataset to study the tracking and segmenting objects in complex environments. MOSE contains 2,149 video clips and 5,200 objects from 36 categories, with 431,725 high-quality object segmentation masks. The most notable feature of MOSE dataset is complex scenes with crowded and occluded objects. Source: MOSE: A New Dataset for Video Object Segmentation in Complex Scenes
BL30K is a synthetic dataset rendered using Blender with ShapeNet's data. We break the dataset into six segments, each with approximately 5K videos. The videos are organized in a similar format as DAVIS and YouTubeVOS, so dataloaders for those datasets can be used directly. Each video is 160 frames long, and each frame has a resolution of 768*512. There are 3-5 objects per video, and each object has a random smooth trajectory -- we tried to optimize the trajectories in a greedy fashion to minimize object intersection (not guaranteed), with occlusions still possible (happen a lot in reality). See MiVOS for details.
PUMaVOS is a dataset of challenging and practical use cases inspired by the movie production industry. Partial and Unusual Masks for Video Object Segmentation (PUMaVOS) dataset has the following properties: - 24 videos, 21187 densely-annotated frames; - Covers complex practical use cases such as object parts, frequent occlusions, fast motion, deformable objects and more; - Average length of the video is 883 frames or 29s, with the longer ones spanning 1min; - Fully densely annotated at 30FPS; - Benchmark-oriented: no separation into training/test, designed to be as diverse as possible to test your models; - 100% open and free to download.
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