…Since the dataset is collected ‘in the wild’, the speech segments are corrupted with real world noise including laughter, cross-talk, channel effects, music and other sounds.
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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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Synthetic dataset for polyp segmentation. It is the first dataset generated using zero annotations from medical professionals. The dataset is composed of 20 000 images with a resolution of 500×500.
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…The dataset culminates in a complex toy car segment representative of many challenging real-world scenarios.
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…Updated ROI segmentation and bounding boxes, and pathologic diagnosis for training data are also included. Finally, the ROI annotations for the abnormalities in the DDSM were provided to indicate a general position of lesions, but not a precise segmentation for them. Therefore, many researchers must implement segmentation algorithms for accurate feature extraction.
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USC-GRAD-STDdb comprises 115 video segments containing more than 25,000 annotated frames of HD 720p resolution (≈1280x720) with small objects of interest from 16 (≈4x4) to 256 (≈16x16) as pixel area.
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…Video Acquisition: 1920x1080 at 12.00 fps 11 training videos and 9 validation/test videos 8857 video segments temporally annotated indicating the verbs which describe the actions performed 64349 active
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…The dataset can be used for multi-label based image classification, multi-label based image retrieval, and image segmentation.
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…Paper: How to Collect Segmentations for Biomedical Images? A Benchmark Evaluating the Performance of Experts, Crowdsourced Non-Experts, and Algorithms
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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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…Annotation: Scene class label and Temporal segments label. Metadata: including textual scene descriptions, weather conditions, capture time, and GPS information.
…A convolutional neural network (CNN) was used on these large and noisy audio recordings to automatically extract segments of sound containing a baboon vocal production by following the method of Bonafos The resulting dataset consists of one-second to several-minute wav files of automatically detected vocalizations segments. The dataset consists of the segments predicted by the CNN to contain a baboon vocalization. Windows containing signal less than one second apart were merged into a single vocalization. If the time windows that follow a vocalization also contain a vocalization, then the signal they contain is added to the first segment for which a vocalization has been detected. As soon as a one-second segment no longer contains a signal corresponding to a vocalization, the wav file is closed.
…The videos in the dataset are divided into 5-second segments to reduce the complexity of annotation.
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…Each instance furnishes details about the extended radio galaxy class, a bounding box covering all components, a pixel-level segmentation mask, and the keypoint position of the corresponding infrared host
…This dense visual grounding takes the form of a mouse trace segment per word and is unique to our data.
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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
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…IDs and URLs of the GIFs and the videos are provided, along with temporal alignment of GIF segments to their source videos.
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…Transcripts: The transcripts cover contiguous 5 or 10-minute segments from recorded conversations. Speaker Awareness: All speakers were aware that they were being recorded.
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…With 4600 images and ∼26 000 segmented cells, our collection offers an unparalleled heterogeneous training dataset for cell biology deep learning application development.
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…Inspiration Could the MODIS true-colour satellite images be utilized for detecting dust storms with higher accuracy and segmentation capability? The associated notebook simply presents the image data visualization, statistical data augmentation and a U-Net-based model to detect dust storms in a semantic segment fashion. Research Ideas Would the latest state-of-the-art segmentation models increase the performance of detecting dust storms in satellite true-colour images? Few-shot learning for dust storm segmentation and related self-supervised learning techniques What is the role of ensemble learning in improving model performance?
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…Dataset can be easily used for supervised classification, out-of-distribution detection (near and far), unsupervised learning and modulation pattern segmentation.
…accompaniment and the singing voice recorded as left and right channels, respectively, Manual annotations of pitch contours in semitone, indices and types for unvoiced frames, lyrics, and vocal/non-vocal segments
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…In order to ease automatic speech segmentation, we carried out the recordings in a anechoic room, with walls covered by sound wave-absorbing materials.
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…It provides data for the analysis of the complete inertial pose pipeline, from raw measurements, to sensor-to-segment calibration, multi-sensor fusion, skeleton kinematics, to the complete human pose.
…The dataset includes RGB images, Depth images, 6D Pose of objects, segmentation mask (all and visible), COCO Json annotation, camera transformations, and 3D model of all objects.
…Although it is designed to be beneficial for several machine learning tasks, it primarily aims to benchmark weakly supervised pixel-level semantic segmentation learning methods.
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…Additionally, the complete TextGrid files containing the segmentation information of those sessions are also included. The size of the uncompressed dataset is 15GB.
…Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks.
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…This database targets two main research problems in the document image analysis field (i) symbol recognition and spotting in line drawing images (floorplans and electrical diagrams) (ii) character segmentation
…We benchmark four foundational video understanding tasks: action recognition, action segmentation, object detection and multi-object tracking.
…Meta-data and annotations associated to the dataset varies from anatomical landmark and description of the procedure labeling, tools segmentation masks, COLMAP 3D reconstructions, simulated sequences with
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…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
…EPIC-KITCHENS-55), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments
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…for analysis and activity recognition consisting of continues recordings of combined activities, such as walking, running, taking stairs up and down, sitting down, and so on; and the data recorded are segmented
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…During the game, each player can access various observations, including the first-person view screen pixels, the corresponding depth-map and segmentation-map (pixel-wise object labels), the bird-view maze
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…The cross-domain outdoor to indoor transition segments are especially challenging because of realistic sensor behavior such as GNSS degradation and dropouts, changes in the measured magnetic field, and flight scenario, such as the transition data, which requires sensor switching, or the Mars analog data with higher velocities, multiple touchdowns, challenging ground structures or constant velocity segments
…This (FS-02) edition of the FEARLESS STEPS Challenge includes the following 6 tasks --- TASK 1: Speech Activity Detection (SAD) TASK 2: Speaker Identification (using Speaker Segments Track 2: ASR using Diarized Segments (ASR_track2)
…contains 20 common workshop tools, and for each object: - a watertight triangular surface mesh; - a synthetic colored surface point-cloud; - ground truth inertial parameters; - ground truth part-level segmentation by Open3D element vertex 2000 property float32 x property float32 y property float32 z property float32 red property float32 green property float32 blue property uint8 segmentation please cite our paper: @inproceedings{Nadeau_PartSegForInertialIdent_2023, AUTHOR = {Philippe Nadeau AND Matthew Giamou AND Jonathan Kelly}, TITLE = { {The Sum of Its Parts: Visual Part Segmentation
…natural language generation challenge which consists of mapping the sets of triplets to text, including referring expression generation, aggregation, lexicalization, surface realization, and sentence segmentation
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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.
…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.
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…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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…Three human raters segmented the resection cavity on partially overlapping subsets of EPISURG: Rater 1: 133 subjects (researcher in neuroimaging) Rater 2: 34 subjects (clinical research fellow) Rater dataset for your research please cite the following publications: Pérez-García F., Rodionov R., Alim-Marvasti A., Sparks R., Duncan J.S., Ourselin S. (2020) Simulation of Brain Resection for Cavity Segmentation
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.
BPCIS is collection of 364 bacterial phase contrast images and corresponding label matrices for instance segmentation. Labels were made according to fluorescence channels where possible.
…It has been utilized towards the development of models able to: (i) extract clinically informative respiratory indicators from regular breathing records, and (ii) identify cough, breath and voice segments
…Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment.
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