…A subset of 1.9M includes diverse annotations types. 15,851,536 boxes on 600 classes 2,785,498 instance segmentations on 350 classes 3,284,280 relationship annotations on 1,466 relationships 675,155
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…Due to variations in retinal morphology, intensity range, and changes in contrast and brightness, designing segmentation and detection methods that can generalize to different disease types is challenging The proposed dataset covers three subsets of scans (Age-related Macular Degeneration, Diabetic Macular Edema, and healthy) and annotations for two types of tasks (semantic segmentation and object detection
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…corrected (e.g. calibrated) radiographic projections, their tomographic reconstructions (based on 37 projections of 256 detector pixels into a 100×100 pixel CT image per slice) and the corresponding set of segmentation
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…WoodScape comprises of four surround view cameras and nine tasks including segmentation, depth estimation, 3D bounding box detection and soiling detection.
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Cata7 is the first cataract surgical instrument dataset for semantic segmentation. The dataset consists of seven videos while each video records a complete cataract surgery.
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…low number of target pixels (RFI) proportional to the total data volume makes this dataset a useful test for RFI detection schemes in radio astronomy and, more generally, anomaly detection or semantic segmentation
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…A case was composed of kinematic data, a video, semantic segmentation of each frame, and workflow annotation.
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The PASCAL-Scribble Dataset is an extension of the PASCAL dataset with scribble annotations for semantic segmentation. The annotations follow two different protocols.
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…These attributes allow the usage of Dark Zurich as a dataset to build models and systems that perform: 1) domain adaptation (unsupervised, weakly supervised or semi-supervised), e.g. for semantic segmentation
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…synthetic video dataset designed to learn and evaluate computer vision models for several video understanding tasks: object detection and multi-object tracking, scene-level and instance-level semantic segmentation
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…The main purpose of OCID is to allow systematic comparison of existing object segmentation methods in scenes with increasing amount of clutter.
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…It consists of an exhaustive list of actions performed while driving and multi-modal labeled images (depth, RGB and IR), with complete annotations for 2D and 3D object detection, instance and semantic segmentation
…With the following topics: Blur Background / Segmented Background / AI generated Background/ Bias of tools during annotation/ Color in Background / Dependent Factor in Background/ LatenSpace Distance of
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…In addition, 23 images were annotated by a team of expert radiologists to include semantic segmentation of radiographic findings.
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…Notably, deep learning methods are employed to obtain a semantic segmentation of aerial images.
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…Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities.
3,233 PAPERS • 141 BENCHMARKS
…Tasks our dataset support: Generaliazable Novel view synthesis (Few shot evaluation) Novel view synthesis (Overfitting evaluation) 6D pose estimation Object editing Depth estimation Semantic Segmentation Instance Segmentation
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LabPics Chemistry Dataset Dataset for computer vision for materials segmentation and classification in chemistry labs, medical labs, and any setting where materials are handled inside containers. In addition to instance segmentation maps, the dataset also includes semantic segmentation maps that give each pixel in the image all the classes to which it belongs.
…In order to ease multitask learning, we provide a pairing of 2D instance segments with 3D bounding boxes.
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…The main objective of this dataset is to create the basis for experimenting with suitable solutions to give a reliable answer to the above questions, or to propose models capable of producing dynamic segmentation
Our project (STPLS3D) aims to provide a large-scale aerial photogrammetry dataset with synthetic and real annotated 3D point clouds for semantic and instance segmentation tasks.
32 PAPERS • 3 BENCHMARKS