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3. The COCO-Stuff dataset The Common Objects in COntext (COCO) [35] dataset is a large-scale dataset of images of high complexity. COCO has been designed to enable the study of thing-thing

... segmentation, and captioning dataset. COCO has several features: Object segmentation; Recognition in context; Superpixel stuff segmentation ...

Welcome to official homepage of the COCO-Stuff [1] dataset. COCO-Stuff augments all 164K images of the popular COCO [2] dataset with ...

Dec 12, 2016 · To understand stuff and things in context we introduce COCO-Stuff, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes. We introduce an efficient stuff annotation protocol based on superpixels, which leverages the original thing annotations.

The Common Objects in COntext-stuff (COCO-stuff) dataset is a dataset for scene understanding tasks like semantic segmentation, object detection and image captioning. It is constructed by annotating the original COCO dataset, which originally annotated things while neglecting stuff annotations. There are 164k images in COCO-stuff dataset that span over 172 …

COCO-Stuff 10K dataset: Our first dataset, annotated by 10 in-house annotators at the University of Edinburgh. It includes 10K images from the training set of ...

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COCO-Stuff augments the COCO dataset with pixel-level stuff annotations for 10,000 images ... annotation benchmark coco segmentation things captioning stuff ...

The Common Objects in COntext-stuff (COCO-stuff) dataset is a dataset for scene understanding tasks like semantic segmentation, object detection and image captioning. It is constructed by annotating the original COCO dataset, which originally annotated things while neglecting stuff annotations. There are 164k images in COCO-stuff dataset that span over 172 categories including 80 things, 91 ...

Jul 29, 2021 · This article covers everything you need to know about the popular Microsoft COCO dataset that is widely used for machine learning Projects. We will cover what you can do with MS COCO and what makes it different from alternatives such as Google’s OID (Open Images Dataset).

COCO-Stuff augments the popular COCO [2] dataset with pixel-level stuff annotations. These annotations can be used for scene understanding tasks ...

Stuff 1, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes. We introduce an efficient stuff ...

29.07.2021 · 91 stuff categories, where “COCO stuff” includes materials and objects with no clear boundaries (sky, street, grass, etc.) that provide significant contextual information. 5 captions per image 250’000 people with 17 different keypoints, popularly used for Pose Estimation

16.11.2019 · To use this dataset you will need to download the images (18+1 GB!) and annotations of the trainval sets. To download earlier versions of this dataset, please visit the COCO 2017 Stuff Segmentation Challenge or COCO-Stuff 10K.. Caffe-compatible stuff-thing maps We suggest using the stuffthingmaps, as they provide all stuff and thing labels in a single .png file per image.

12.12.2016 · Furthermore, we use COCO-Stuff to analyze: (a) the importance of stuff and thing classes in terms of their surface cover and how frequently they are mentioned in image captions; (b) the spatial relations between stuff and things, highlighting the rich contextual relations that make our dataset unique; (c) the performance of a modern semantic segmentation method on stuff …

The Common Objects in COntext-stuff (COCO-stuff) dataset is a dataset for scene understanding tasks like semantic segmentation, object detection and image ...

The COCO-Stuff dataset The Common Objects in COntext (COCO) [35] dataset is a large-scale dataset of images of high complexity. COCO has been designed to enable the study of thing-thing interactions, and features images of complex scenes with many small objects, annotated with very detailed outlines.

21.09.2019 · COCO Dataset Stuff Segmentation Challenge. Abstract: In computer vision, image segmentation is a method in which a digital image is divided/partitioned into multiple set of pixels which are called super-pixels, stuff segmentation challenge is a newly introduced task in which we have to segment out stuff out of the digital image.

Nov 16, 2019 · The COCO-Stuff dataset. Holger Caesar, Jasper Uijlings, Vittorio Ferrari. Welcome to official homepage of the COCO-Stuff [1] dataset. COCO-Stuff augments all 164K images of the popular COCO [2] dataset with pixel-level stuff annotations.

cocostuff10k - The official homepage of the (outdated) COCO-Stuff 10K dataset. ... The current release of COCO-Stuff-10K publishes both the training and test ...

To understand stuff and things in context we introduce COCO-Stuff, which augments all 164K images of the COCO 2017 dataset with pixel-wise ...

2014. COCO-Stuff ( Common Objects in COntext-stuff) The Common Objects in COntext-stuff (COCO-stuff) dataset is a dataset for scene understanding tasks like semantic segmentation, object detection and image captioning. It is constructed by annotating the original COCO dataset, which originally annotated things while neglecting stuff annotations.

Fortunately, the COCO-Stuff dataset [7] provides labels and segmentations for all of the "stuff" in the images from COCO. The "thing" vs "stuff" distinction ...