…This dataset contains 180 subjects preprocessed images, and each subject comprises a brain MR image and a brain CT image with corresponding segmentation label.
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…The proposed dataset will allow proper evaluation of salient edges and semantic segmentation of images focusing on the street view perspective
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Meta Omnium is a dataset-of-datasets spanning multiple vision tasks including recognition, keypoint localization, semantic segmentation and regression.
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The PAX-Ray++ dataset uses pseudo-labeled thorax CTs to enable the segmentation of anatomy in Chest X-Rays.
This dataset aims at evaluating the License Plate Character Segmentation (LPCS) problem. The experimental results of the paper Benchmark for License Plate Character Segmentation were obtained using a dataset providing 101 on-track vehicles captured during the day.
6 PAPERS • 1 BENCHMARK
…The images of the dataset were extracted from the public databases DermIS and DermQuest, along with manual segmentations of the lesions. Clausi, "Automatic segmentation of skin lesions from dermatological photographs using a joint probabilistic texture distinctiveness approach", IEEE Transactions on Biomedical Engineering 2 Amelard, R.,
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…Sequences are annotated with more than 100K coarse and 1M fine-grained action segments, and 18M 3D hand poses. We benchmark on three action understanding tasks: recognition, anticipation and temporal segmentation. Additionally, we propose a novel task of detecting mistakes.
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The dataset contains two subsets of synthetic, semantically segmented road-scene images, which have been created for developing and applying the methodology described in the paper "A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird’s Eye View" (IEEE Xplore, arXiv, YouTube) The dataset can be used through the
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The dataset contains information on what video segments a specific user considers a highlight. The data consists of YouTube videos, from which gifs.com users manually extracted their highlights, by creating GIFs from a segment of the full video.
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UAVid is a high-resolution UAV semantic segmentation dataset as a complement, which brings new challenges, including large scale variation, moving object recognition and temporal consistency preservation
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…Context Dataset, a composition of two inter-compatible large-scale and versatile image datasets focusing on manned aircraft and UAVs, is intended for training and evaluating classification, detection and segmentation
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Pavementscapes is a large-scale dataset to develop and evaluate methods for pavement damage segmentation.
MyFood Dataset is an image database for segmenting images of Brazilian foods. Composed of 9 classes: rice, beans, boiled egg, fried egg, pasta, salad, roasted meat, apple and chicken breast.
This dataset contains 1200 images (1000 WLI images and 200 FICE images) with fine-grained segmentation annotations. The training set consists of 1000 images, and the test set consists of 200 images.
7 PAPERS • 1 BENCHMARK
DeepSportradar currently supports four challenging tasks related to basketball: ball 3D localization, camera calibration, player instance segmentation and player re-identification.
…It is annotated with three types of information: marking of the dialogue act segment boundaries, marking of the dialogue acts and marking of correspondences between dialogue acts.
8 PAPERS • 1 BENCHMARK
SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation.
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PerSeg is a dataset for personalized segmentation. The raw images are collect from the training data of subject driven diffusion models: DreamBooth, Textual Inversion, and Custom Diffusion.
5 PAPERS • 1 BENCHMARK
…It provides both full-length predator chats from PervertedJustice as well as short segments of non-predator chats. Together these can be used to evaluate eSPD systems.
…The dataset structure resembles the tasks of (1) segmenting sentences within a document to the set of propositions, and (2) classifying the entailment relation of each proposition with respect to a different
…Each frame has a semantic segmentation of the objects in the scene and information about the camera pose. It is composed by 415 sequences captured in 254 different spaces, in 41 different buildings.
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…Overall, the BANDON dataset contains 2283 pairs of images, 2283 change labels,1891 BT-flows labels, 1891 pairs of segmentation labels, and 1891 pair of ST-offsets labels (test sets do not provide auxiliary
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…The task to learn is not just semantic segmentation but also the motion status of the objects.
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…It can be used for detection and segmentation of transmission towers and power lines.
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…ATM'22 is a multi-site, multi-domain dataset for pulmonary airway segmentation.
…Each image is annotated with up to eight vanishing points, and pre-extracted line segments are provided which act as data points for a robust estimator.
…Each scene is a residential building consisting of multiple rooms and floor levels, and is annotated with surface construction, camera poses, and semantic segmentation.
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…The comparison is performed not only by evaluating the quality of neuron segmentations, but also by assessing the accuracy of detecting synapses and identifying synaptic partners. The challenge is carried out on three large and diverse datasets from adult Drosophila melanogaster brain tissue, comprising neuron segmentation ground truth and annotations for synaptic connections.
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The University of Massachusetts Amherst citation field extraction dataset contains labels and segments for extracted citations from articles found on arXiv. Each citation string was labeled hierarchically, separating coarse-grain and fine-grain labeled segments. Dataset introduced in the following paper: Sam Anzaroot and Andrew McCallum.
…Object segmentation masks, object poses and object attributes are provided. In addition, synthetic images generated using 330 3D object models are used to augment the dataset. FewSOL dataset can be used to study a set of few-shot object recognition problems such as classification, detection and segmentation, shape reconstruction, pose estimation, keypoint correspondences and
…The main use of this data set is the study of segmentation algorithms that can separate individual nucleus instances in an accurate way, regardless of their shape and cell density. The collection has around 23,000 single nuclei manually annotated to establish a ground truth collection for segmentation evaluation.
…lyrics encode an important part of the semantics of a song, the authors focus on the description of the methods they proposed to extract relevant information from the lyrics, such as their structure segmentation can be exploited by music search engines and music professionals (e.g. journalists, radio presenters) to better handle large collections of lyrics, allowing an intelligent browsing, categorization and segmentation
…By collecting data in simulations, multi-modal sensor data and precise ground truth labels are obtainable such as the RGB image, depth image, semantic segmentation, change segmentation, camera poses, and
4 PAPERS • 2 BENCHMARKS
…Basically, "rationales" are segments of the text that support an annotator's classification. Then the rationales would be segments of the text that support the claim (by an annotator) that the review is, indeed, positive. Here are some examples of positive rationales (the segments enclosed by double square brackets): [[you will enjoy the hell out of]] American Pie. fortunately, they [[managed to do it in an interesting
…The ADAM challenge focuses on the investigation and development of algorithms associated with the diagnosis of Age-related Macular degeneration (AMD) and segmentation of lesions in fundus photos from AMD We invite the medical image analysis community to participate by developing and testing existing and novel automated fundus classification and segmentation methods. Task 2: Detection and segmentation of optic disc. Task 3: Localization of fovea. Task 4: Detection and Segmentation of lesions from fundus images.
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…For each image in the test set, you must segment the regions of each cloud formation label. Each image has at least one cloud formation, and can possibly contain up to all all four. The segment for each cloud formation label for an image is encoded into a single row, even if there are several non-contiguous areas of the same formation in an image. Files train.csv - the run length encoded segmentations for each image-label pair in the train_images train_images.zip - folder of training images test_images.zip - folder of test images; your task is to predict the segmentations masks of each of the 4 cloud types (labels) for each image.
2 PAPERS • 1 BENCHMARK
Introduced by Da et al. in DigestPath: a Benchmark Dataset with Challenge Review for the Pathological Detection and Segmentation of Digestive-System Grand-Challenge Page 1. Colonoscopy tissue segment dataset Colonoscopy pathology examination can find cells of early-stage colon tumor from small tissue slices. Here we propose a challenge task on automatic colonoscopy tissue segmentation and screening, aiming at automatic lesion segmentation and classification of the whole tissue (benign vs. malignant). DigestPath: a Benchmark Dataset with Challenge Review for the Pathological Detection and Segmentation of Digestive-System[J]. Medical Image Analysis, 2022: 102485.
22 PAPERS • 1 BENCHMARK
…In this dataset, we report the extracted segments used for an analysis of R peak detection algorithms during high intensity exercise. For each subject, 5 segments of 20 s were extracted from the ECG recordings and chosen based on different phases of the maximal exercise test (i.e., before and after the so-called second ventilatory threshold seg1 --> [VT2-50,VT2-30] seg2 --> [VT2+60,VT2+80] seg3 --> [VO2max-50,VO2max-30] seg4 --> [VO2max-10,VO2max+10] seg5 --> [VO2max+60,VO2max+80] The R peak locations were manually annotated in all segments Only segment 5 of subject 9 could not be annotated since there was a problem with the input signal. So, the total number of segments extracted were 20 * 5 - 1 = 99. Format of the extracted dataset The dataset is divided in two main folders: The folder `ecg_segments/` contains the ECG signals saved in two formats, `.csv` and `.mat`.
…pairs primarily aligned with English (39 out of 41) and mined using the parallel-data-crawling tool Bitextor which includes downloading documents, preprocessing and normalization, aligning documents and segments
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…The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas.
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The Common Objects in COntext-stuff (COCO-stuff) dataset is a dataset for scene understanding tasks like semantic segmentation, object detection and image captioning.
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…In total, 103 scenes of 10 common off-the-shelf objects with rich textures are recorded, with each frame annotated with a per-pixel semantic segmentation and ground-truth object poses provided by a commercial
…About 250,000 frames (in 137 approximately minute long segments) with a total of 350,000 bounding boxes and 2300 unique pedestrians were annotated.
3 PAPERS • 1 BENCHMARK
The RIT-18 dataset was built for the semantic segmentation of remote sensing imagery.
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LVOS is a dataset for long-term video object segmentation (VOS). It consists of 220 videos with a total duration of 421 minutes.
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DensePASS - a novel densely annotated dataset for panoramic segmentation under cross-domain conditions, specifically built to study the Pinhole-to-Panoramic transfer and accompanied with pinhole camera
38 PAPERS • 1 BENCHMARK
The unsupervised Labeled Lane MArkerS dataset (LLAMAS) is a dataset for lane detection and segmentation.
…Leaf wood labels were transferred from contemporaneous (2021) TLS acquisition, for which segmentation was done using LeWoS and onscreen post correction.