The goal of video instance segmentation is simultaneous detection, segmentation and tracking of instances in videos. In words, it is the first time that the image instance segmentation problem is extended to the video domain.
To facilitate research on this new task, a large-scale benchmark called YouTube-VIS, which consists of 2,883 high-resolution YouTube videos, a 40-category label set and 131k high-quality instance masks is built.
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Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue. More detail is at the dataset home page.
YouTubeVIS is a new dataset tailored for tasks like simultaneous detection, segmentation and tracking of object instances in videos and is collected based on the current largest video object segmentation dataset YouTubeVOS. Source: YouTubeVIS
OVIS is a new large scale benchmark dataset for video instance segmentation task. It is designed with the philosophy of perceiving object occlusions in videos, which could reveal the complexity and the diversity of real-world scenes. OVIS consists of: * 296k high-quality instance masks * 25 commonly seen semantic categories * 901 videos with severe object occlusions * 5,223 unique instances If the description or image is from a different paper, please refer to it as follows: Source: http://songbai.site/ovis/
3,859 high-resolution YouTube videos, 2,985 training videos, 421 validation videos and 453 test videos. An improved 40-category label set by merging eagle and owl into bird, ape into monkey, deleting hands, and adding flying disc, squirrel and whale 8,171 unique video instances 232k high-quality manual annotations
UVO is a new benchmark for open-world class-agnostic object segmentation in videos. Besides shifting the problem focus to the open-world setup, UVO is significantly larger, providing approximately 8 times more videos compared with DAVIS, and 7 times more mask (instance) annotations per video compared with YouTube-VOS and YouTube-VIS. UVO is also more challenging as it includes many videos with crowded scenes and complex background motions. Some highlights of the dataset include: - High quality instance masks densely annotated at 30 fps on 1024 YouTube videos and 1fps on 10337 videos from Kinetics dataset - Open-world: annotating all objects in each video, 13.5 objects per video on average - Diverse object categories: 57% of objects are not covered by COCO categories
BURST is a benchmark suite built upon TAO that requires tracking and segmenting multiple objects from camera video. The benchmark contains 6 different sub-tasks divided into 2 groups that all share the same data for training/validation/testing. ##### Class-guided 1. Common: Track and segment all objects belonging to a set of 78 common classes (based on the COCO class set) 2. Long-tail: Track and segment all objects belonging to an extended set of 482 object classes (based on the LVIS class set) 3. Open-world: Methods are only allowed to use the annotations of the 78 common classes during training, but during inference they are expected to track and segment all 482 object classes (class label predictions are not required) ##### Exemplar-guided 4. Mask: Track and segment all objects in the video for which the first-frame object masks are given. This task is identical to Video Object Segmentation (VOS). 5. Box: Track and segment all objects in the video for which the first-frame object bounding-boxes are given. 6. Point: Track and segment all objects in the video for which we are only given the (x,y) point coordinates of the mask centroid in the first-frame in which the objects appear. An illustration of the task hierarchy is given here and a detailed explanation is given in Sec. 5 of the dataset paper
Video object segmentation has been studied extensively in the past decade due to its importance in understanding video spatial-temporal structures as well as its value in industrial applications. Recently, data-driven algorithms (e.g. deep learning) have become the dominant approach to computer vision problems and one of the most important keys to their successes is the availability of large-scale datasets. Previously, we presented the first large-scale video object segmentation dataset named YouTubeVOS and hosted the Large-scale Video Object Segmentation Challenge in conjuction with ECCV 2018, ICCV 2019 and CVPR 2021. This year, we are thrilled to invite you to the 4th Large-scale Video Object Segmentation Challenge in conjunction with CVPR 2022. The benchmark would be an augmented version of the YouTubeVOS dataset with more annotations. Some incorrect annotations are also corrected. For more details, check our website for the workshop and challenge.
While Video Instance Segmentation (VIS) has seen rapid progress, current approaches struggle to predict high-quality masks with accurate boundary details. To tackle this issue, we identify that the coarse boundary annotations of the popular YouTube-VIS dataset constitute a major limiting factor. To benchmark high-quality mask predictions for VIS, we introduce the HQ-YTVIS dataset as well as Tube-Boundary AP in ECCV 2022. HQ-YTVIS consists of a manually re-annotated test set and our automatically refined training data, which provides training, validation and testing support to facilitate future development of VIS methods aiming at higher mask quality.
