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1,506 results for “objects”
Phyloseq R object accompanying the paper Temporal Dynamics Cloacal Microbiota 16S metataxonomics
<p>This ready to load <strong>phyloseq</strong> R S4 object contains the ASV table, taxonomy table and sample metadata. This data was build using the DaDa2 (version 1.6.0) and phyloseq (version 1.223) R packages using our raw MiSeq PE300 sequencing data deposited at NCBI-SRA under BioProject: PRJNA673103.</p> <p>The accompanying (peer-reviewed) scientific article can be found here: </p> <ul> <li>https://www.frontiersin.org/articles/10.3389/fmicb.2020.626713/abstract </li> <li>DOI: 10.3389/fmicb.2020.626713</li> </ul> <p> </p> <p><strong>Study/paper</strong> </p> <p>J. Schreuder, F.C. Velkers, A. Bossers, R.J. Bouwstra, W.F. de Boer, P. van Hooft, J.A. Stegeman, S.D. Jurburg.</p> <p>Associations between animal health and performance, and the host’s microbiota have been recently established. In poultry, changes in the intestinal microbiota have been linked to housing conditions and host development, but how the intestinal microbiota respond to environmental changes under farm conditions is less well understood. To gain insight into the microbial responses following a change in the host’s immediate environment, we monitored four indoor flocks of adult laying chickens three times over 16 weeks, during which two flocks were given access to an outdoor range, and two were kept indoors. To assess changes in the chickens’ microbiota over time, we collected cloacal swabs of 10 hens per flock and performed 16S rRNA gene amplicon sequencing.<br> The poultry house (i.e., the stable in which flocks were housed) and sampling time explained 9.2 % and 4.4 % of the variation in the microbial community composition of the flocks, respectively. Remarkably, access to an outdoor range had no detectable effect on microbial community composition, the variability of microbiota among chickens of the same flock, or microbiota richness, but the microbiota of outdoor flocks became more even over time. Fluctuations in the composition of the microbiota over time within each poultry house were mainly driven by turnover in rare, rather than dominant, taxa and were unique for each flock. We identified 16 amplicon sequence variants that were differentially abundant over time between indoor and outdoor housed chickens, however none were consistently higher or lower across all chickens of one housing type over time. Our study shows that cloacal microbiota community composition in adult layers is stable following a sudden change in environment, and that temporal fluctuations are unique to each flock. By exploring microbiota of adult poultry flocks within commercial settings, our study sheds light on how the chickens’ immediate environment affects the microbiota composition.</p>
Histograms of local features for the object dataset
<p>Pictures used in the experiments were acquired as frames from an interaction with an iCub robot, through an RGBD sensor (red, green, and blue camera coupled with a depth sensor). The acquisition was performed and is described in more details by Lyubova and Filliat. Each image contains one visual object and possibly the hand of the operator.</p> <p><br /> Images contains objects from a subset of 10 objects each appearing in more than a thousand frames.During the acquisition, the objects are moved and rotated. Hence they are presented from distinct points of view and they may be partially cluttered by the hand of the operator.</p> <p> </p> <p>For more information on the format please look at https://github.com/omangin/multimodal/blob/master/db/models/objects.py.</p> <p> </p>
Dataset supplementing Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006
<p>These data supplement the publication</p> <p>Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006</p> <p>and be used freely for scientific purposes provided the aforementioned paper is appropriately cited.</p> <p>Note that the image files cannot be provided on this site due to copyright restrictions.</p> <p>The dataset contains the following files:</p> <p>maps_01.mat - maps_72.mat:</p> <p>For each image the 6 maps used in the paper are contained, the maps of experiment 1 are labelled as in the paper (AWS, OOM, nOOM, PVL,UNI), AWS2 is the AWS map for the modified stimuli of experiments 2 and 3.</p> <p>exp?_fixations.mat contains all fixations of the respective experiment.</p> <p>For experiment 1, there are the variables xFix, yFix, durFix, which contain the x position, the y condition, and the fixation duration of each fixation. Dimensions are images x subjects x fixation number, where the first fixation is the 0th (initial) fixation. The variable condition (image x subject) contains the condition in which the respective image was shown to the subject. For the main analysis only the "0" condition was used, refer to the paper's appendix for the other conditions.</p> <p>For experiment 2 and 3, variables are called xFixByImage, yFixByImage, dFixByImage and the dimensions are subject x image x fixation number. In addition tFixByImage contains the start of the fixation relative to trial onset (negative for the 0th fixation).<br> In both cases, empty entries are filled with nans.</p> <p><br> computeROC.m is a helper function called by other functions.</p> <p><br> figure1.m through figure7.m reproduce the figures from the paper to exemplify data usage.</p> <p> </p>
Dataset supplementing the publication Einhäuser, W., Thomassen, S., & Bendixen, A. (2017). Using binocular rivalry to tag foreground sounds: towards an objective visual measure for auditory multistability. Journal of Vision, 17:34, 1-19.
<p>These files supplement the publication Einhäuser, W., Thomassen, S., & Bendixen, A. (2017). Using binocular rivalry to tag foreground sounds: towards an objective visual measure for auditory multistability. Journal of Vision, 17:34, 1-19. The data are free for scientific use, provided this reference is appropriately cited.</p> <p>exp1_data.mat contains all the data of experiment 1 as cell arrays of size 8x16x8 (subject x block x trial) or 8x16 (subject x block). Specifically:<br> xEye: the horizontal eye position in raw (pixel coordinates)<br> gain: the OKN slow phase gain computed from the xEye data as described in the paper; in audio-visual blocks the sign is chosen such that positive gain corresponds to the direction of the grating associated with the low tone; in unambiguous visual blocks (1,16) positive sign corresponds to the direction of the grating.<br> ixLow, ixHigh, ixNone, ixBoth: indices for xEye and gain of the same subject and block for which the button corresponding to the low tone, the high tone, both buttons or no button was pressed.</p> <p>exp2_data.mat and exp3_data.mat contain the data of experiment 2 and experiment 3, respectively, and are organized analogously to exp1_data.mat.</p> <p>figure3.m through figure6.m use these data to plot the respective paper figures to exemplify usage of the data.</p> <p> </p>
A modular set of synthetic spectral energy distributions for young stellar objects - Robitaille (2017) - v1.1 [Hyperion files]
<p>These are the input and output files for the radiative transfer code (Hyperion) for the model sets presented in</p> <p><em>A modular set of synthetic spectral energy distributions for young stellar objects</em>, Robitaille (2017)</p> <p>Each model set is provided as a single tar file. Each tar file expands to <strong>grids-1.1/<set name></strong>, so if you expand all tar files in the same folder, you will end up with a single <strong>grids-1.1</strong> folder with 18 sub-folders, one for each model set.</p> <p>For a given model set, the files are as follows:</p> <ul> <li>grids-1.1/<set name>/input - input Hyperion files</li> <li>grids-1.1/<set name>/log - log files from Hyperion</li> <li>grids-1.1/<set name>/output - output Hyperion files</li> <li>grids-1.1/<set name>/par - parameters for each model</li> <li>grids-1.1/<set name>/ranges.conf - ranges of parameters varied in the model set</li> <li>grids-1.1/<set name>/parameters.hdf5 - table of parameters for all models</li> <li>grids-1.1/<set name>/d03_5.5_3.0_A_sub.hdf5 - dust file used for the models</li> </ul> <p>Given the large number of models for some of the model sets, the models are not all stored directly inside the par, input, output or log directories - instead these directories contain folders formed from the first two characters (forced to lowercase) of the names of the models they contain. For example, a3 contains all models whose name starts with a3 or A3. This was done to avoid having too many files in a single folder which can cause issues on certain file systems.</p> <p>For the Hyperion input and output files, in some cases an _sed file is present. In these cases, the output SEDs (and polarization spectra) should be read from the _sed file, not the original output file. This is the case for all models that are in a set for which the ambient medium was present, as described in §4.2.3 of Robitaille (2017). Furthermore, in some cases the SED file is called _sed_noscat to indicate that scattering was not included, as described in §5.1 of Robitaille (2017).</p> <p>To avoid taking up too much disk space, the Hyperion HDF5 input/output files use external links to refer to each other and to the dust file. To make sure the links work, you should do all operations with the input/output files from the directory containing <strong>grids-1.1</strong>. For example, to open a Hyperion output file, you would need to do (in Python):</p> <p> In [1]: from hyperion.model import ModelOutput</p> <p> In [2]: mo = ModelOutput('grids-1.1/s---s-i/output/a3/A3kQmQtj.rtout')</p> <p>A notebook with examples of reading in the output files can be found here:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models/blob/master/notebook_raw/reading_raw_files.ipynb</p> <p>More information on using Hyperion, including reading input/output files, can also be found at http://docs.hyperion-rt.org</p> <p>For <strong>announcements</strong> of new versions of these models, you can subscribe to the following mailing list:</p> <p>https://groups.google.com/forum/#!forum/protostars</p> <p>For <strong>questions or issues</strong> using these models, you can open a GitHub issue in the companion repository:</p> <p>https://github.com/hyperion-rt/paper-2017-sed-models/issues/new</p>
Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila - Processed Data
<p>Processed data and code for plotting figures for the paper:</p><p>"Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila", by Kit D. Longden, Edward M. Rogers, Aljoscha Nern, Heather Dionne, Michael B. Reiser.</p><p>Data (compressed results folder) and plotting code (compressed src folder) are MATLAB files (see READ_ME for version information and toolboxes). The Source Data excel file also contains the data plotted in the paper figures.</p>
Next-generation 3D object detection and tracking for self-driving vehicles using object velocity
<p>The synthetic dataset was generated using KITTI-like specifications and annotations format. It is comprised by the training and testing sets, that include KITTI standard folders: label_2, image_2 and calib. Furthermore, there is a velodyne file for each of the following use cases:</p><ul><li>Point cloud 1: (x,y,z, (Float)Radial_Velocity): this point cloud has the relative radial velocity as an additional feature for each point. File: velodyne_radial_velocity;</li><li>Point cloud 2: (x,y,z,(Float)Absolute_Speed): in this point cloud, every point has the absolute speed of the object as the additional feature. File: velodyne_abs_speed;</li><li>Point cloud 3: (x,y,z,(Bool)Is_Moving): the additional feature of this point cloud is a Boolean value that is set to 1.0 if the object is moving; contrariwise, it is set to 0.0 for static objects. File: velodyne_is_moving;</li><li>Point cloud 4: (x,y,z,0): no additional feature information. If desired, requires post-processing to convert to (x,y,z) or changing the toolbox point cloud configuration to not consider the additional feature. File: velodyne_xyz;</li></ul><p>Additionally, the detections generated with the OpenPCDet toolbox and Second-IoU model are provided.</p><p>This work was made as part of a master thesis of Informatics Engineering in the University of Aveiro.</p>
Eurasian tree sparrows are more food neophobic and habituate to novel objects more slowly than house sparrows
<p>Introductions of non-native species throughout the world have had severe ecological consequences. However, most research has focused on environmental and ecological factors that allow for introduced species to succeed and become invasive, with fewer studies assessing the roles of behavioural and cognitive traits. To help fill this knowledge gap, we studied neophobia, an aversion towards novelty, in the non-native Eurasian tree sparrow (<em>Passer montanus</em>), and compared results to previous work in a more successful invasive congener, the house sparrow (<em>Passer domesticus</em>). We assessed the neophobia of wild-caught Eurasian tree sparrows by measuring their responses to novel objects and novel foods and their ability to habituate to initially novel objects. We predicted that Eurasian tree sparrows, as less successful invaders, would overall be more neophobic than house sparrows. Although we did not observe differences in neophobia towards novel objects in the two species, Eurasian tree sparrows were significantly less willing to try novel foods than house sparrows. Eurasian tree sparrows were also slower to habituate to repeated presentations of the same initially novel object compared to house sparrows. Multiple factors certainly influence invasion success, but our results suggest that neophobia might limit the success of an introduced species in novel environments.</p>
PROCRAFT Final Meeting - The historian facing aeronautical heritage or historical objectivity facing mythology by Jean-Marc Olivier
Open the record for dataset details and reuse information.
VISIONE Feature Repository for VBS: Multi-Modal Features and Detected Objects from VBSLHE Dataset
<p>This repository contains a diverse set of features extracted from the VBSLHE dataset (laparoscopic gynecology) . These features will be utilized in the VISIONE system [Amato et al. 2023, Amato et al. 2022] in the next editions of the Video Browser Showdown (VBS) competition (<a href="https://www.videobrowsershowdown.org/">https://www.videobrowsershowdown.org/</a>). </p> <p>We used a snapshot of the dataset provided by the Medical University of Vienna and Toronto that can be downloaded using the instructions provided at <a href="https://download-dbis.dmi.unibas.ch/mvk/">https://download-dbis.dmi.unibas.ch/mvk/</a>. It comprises 75 video files. We divided each video into video shots with a maximum duration of 5 seconds.</p> <p>This repository is released under a Creative Commons Attribution license. If you use it in any form for your work, please cite the following paper:</p> <blockquote> <p>@inproceedings{amato2023visione, title={VISIONE at Video Browser Showdown 2023}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Falchi, Fabrizio and Gennaro, Claudio and Messina, Nicola and Vadicamo, Lucia and Vairo, Claudio}, booktitle={International Conference on Multimedia Modeling}, pages={615--621}, year={2023}, organization={Springer} } </p> </blockquote> <p> </p> <p>This repository (v2) comprises the following files:</p> <ul> <li><em><strong>msb.tar.gz </strong></em> contains tab-separated files (.tsv) for each video. Each tsv file reports, for each video segment, the timestamp and frame number marking the start/end of the video segment, along with the timestamp of the extracted middle frame and the associated identifier ("id_visione").</li> <li><em><strong>extract-keyframes-from-msb.tar.gz</strong></em> contains a Python script designed to extract the middle frame of each video segment from the MSB files. To run the script successfully, please ensure that you have the original VBSLHE videos available.</li> <li><em><strong>features-aladin.tar.gz†</strong></em><strong> </strong>contains <a href="https://github.com/mesnico/ALADIN">ALADIN</a> [Messina N. et al. 2022] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip-laion.tar.gz†</strong></em> contains <a href="https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K">CLIP ViT-H/14 - LAION-2B </a>[Schuhmann et al. 2022] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip-openai.tar.gz† </strong></em>contains <a href="https://huggingface.co/openai/clip-vit-large-patch14">CLIP ViT-L/14</a> [Radford et al. 2021] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip2video.tar.gz† </strong></em>contains <a href="https://github.com/CryhanFang/CLIP2Video">CLIP2Video</a> [Fang H. et al. 2021] extracted for all the video segments. <strong> </strong></li> <li><em><strong>objects-frcnn-oiv4.tar.gz* </strong></em>contains the objects detected using <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4 [Kuznetsova et al. 2020]).</li> <li><em><strong>objects-mrcnn-lvis.tar.gz*</strong></em> contains the objects detected using Mask R-CNN [He et al. 2017] (trained on LVIS).</li> <li><em><strong>objects-vfnet64-coco.tar.gz*</strong></em> contains the objects detected using VfNet [Zhang et al. 2021] (trained on COCO dataset).</li> </ul> <p>*Please be sure to use the <strong>v2 version </strong>of this repository, since v1 feature files may contain inconsistencies that have now been corrected</p> <p><em><strong>*Note on the object annotations:</strong></em> Within an object archive, there is a jsonl file for each video, where each row contains a record of a video segment (the <em>"_id"</em> corresponds to the <em>"id_visione"</em> used in the msb.tar.gz) . Additionally, there are three arrays representing the objects detected, the corresponding scores, and the bounding boxes. The format of these arrays is as follows:</p> <ul> <li><em>"object_class_names"</em>: vector with the class name of each detected object.</li> <li><em>"object_scores"</em>: scores corresponding to each detected object.</li> <li><em>"object_boxes_yxyx"</em>: bounding boxes of the detected objects in the format <em>(ymin, xmin, ymax, xmax).</em></li> </ul> <p> </p> <p><em><strong>†Note on the cross-modal features: </strong></em>The extracted multi-modal features (ALADIN, CLIPs, CLIP2Video) enable internal searches within the VBSLHE dataset using the query-by-image approach (features can be compared with the dot product). However, to perform searches based on free text, the text needs to be transformed into the joint embedding space according to the specific network being used (see links above). Please be aware that t<strong>he service for transforming text into features is not provided within this repository and should be developed independently using the original feature repositories linked above.</strong></p> <p>We have plans to release the code in the future, allowing the reproduction of the VISIONE system, including the instantiation of all the services to transform text into cross-modal features. However, this work is still in progress, and the code is not currently available.</p> <p> </p> <p><strong>References:</strong></p> <p>[Amato et al. 2023] Amato, G.et al., 2023, January. VISIONE at Video Browser Showdown 2023. In International Conference on Multimedia Modeling (pp. 615-621). Cham: Springer International Publishing.</p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. </p> <p>[Fang H. et al. 2021] Fang H. et al., 2021. Clip2video: Mastering video-text retrieval via image clip. arXiv preprint arXiv:2106.11097.</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. International Journal of Computer Vision, 128(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.</p> <p>[Messina et al. 2022] Messina N. et al., 2022, September. Aladin: distilling fine-grained alignment scores for efficient image-text matching and retrieval. In Proceedings of the 19th International Conference on Content-based Multimedia Indexing (pp. 64-70).</p> <p>[Radford et al. 2021] Radford A. et al., 2021, July. Learning transferable visual models from natural language supervision. In International conference on machine learning (pp. 8748-8763). PMLR.</p> <p>[Schuhmann et al. 2022] Schuhmann C. et al., 2022. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35, pp.25278-25294.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In Proceedings of the IEEE/CV</p>
VISIONE Feature Repository for VBS: Multi-Modal Features and Detected Objects from MVK Dataset
<p>This repository contains a diverse set of features extracted from the marine video (underwater) dataset (MVK) . These features were utilized in the VISIONE system [Amato et al. 2023, Amato et al. 2022] during the latest editions of the Video Browser Showdown (VBS) competition (<a href="https://www.videobrowsershowdown.org/">https://www.videobrowsershowdown.org/</a>). </p> <p>We used a snapshot of the MVK dataset from 2023, that can be downloaded using the instructions provided at <a href="https://download-dbis.dmi.unibas.ch/mvk/">https://download-dbis.dmi.unibas.ch/mvk/</a>. It comprises 1,372 video files. We divided each video into 1 second segments. </p> <p>This repository is released under a Creative Commons Attribution license. If you use it in any form for your work, please cite the following paper:</p> <blockquote> <pre>@inproceedings{amato2023visione, title={VISIONE at Video Browser Showdown 2023}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Falchi, Fabrizio and Gennaro, Claudio and Messina, Nicola and Vadicamo, Lucia and Vairo, Claudio}, booktitle={International Conference on Multimedia Modeling}, pages={615--621}, year={2023}, organization={Springer} }</pre> </blockquote> <p> </p> <p>This repository comprises the following files:</p> <ul> <li><strong><em>msb.tar.gz </em></strong> contains tab-separated files (.tsv) for each video. Each tsv file reports, for each video segment, the timestamp and frame number marking the start/end of the video segment, along with the timestamp of the extracted middle frame and the associated identifier ("id_visione"). </li> <li><em><strong>extract-keyframes-from-msb.tar.gz</strong></em> contains a Python script designed to extract the middle frame of each video segment from the MSB files. To run the script successfully, please ensure that you have the original MVK videos available.</li> <li><strong><em>features-aladin.tar.gz<sup>†</sup></em> </strong>contains <a href="https://github.com/mesnico/ALADIN">ALADIN</a> [Messina N. et al. 2022] features extracted for all the segment's middle frames. </li> <li><em><strong>features-clip-laion.tar.gz<sup>†</sup></strong></em> contains <a href="https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K">CLIP ViT-H/14 - LAION-2B </a>[Schuhmann et al. 2022] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip-openai.tar.gz<sup>†</sup> </strong></em>contains <a href="https://huggingface.co/openai/clip-vit-large-patch14">CLIP ViT-L/14</a> [Radford et al. 2021] features extracted for all the segment's middle frames. </li> <li><em><strong>features-clip2video.tar.gz<sup>†</sup> </strong></em>contains <a href="https://github.com/CryhanFang/CLIP2Video">CLIP2Video</a> [Fang H. et al. 2021] extracted for all the 1s video segments. <strong> </strong></li> <li><em><strong>objects-frcnn-oiv4.tar.gz<sup>*</sup> </strong></em>contains the objects detected using <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4 [Kuznetsova et al. 2020]). </li> <li><em><strong>objects-mrcnn-lvis.tar.gz<sup>*</sup></strong></em> contains the objects detected using Mask R-CNN [He et al. 2017] (trained on LVIS).</li> <li><em><strong>objects-vfnet64-coco.tar.gz<sup>*</sup></strong></em> contains the objects detected using VfNet [Zhang et al. 2021] (trained on COCO dataset).</li> </ul> <p>*Please be sure to use the <strong>v2 version </strong>of this repository, since v1 feature files may contain inconsistencies that have now been corrected</p> <p><em><strong>*Note on the object annotations:</strong></em> Within an object archive, there is a jsonl file for each video, where each row contains a record of a video segment (the <em>"_id"</em> corresponds to the <em>"id_visione"</em> used in the msb.tar.gz) . Additionally, there are three arrays representing the objects detected, the corresponding scores, and the bounding boxes. The format of these arrays is as follows:</p> <ul> <li><em>"object_class_names"</em>: vector with the class name of each detected object.</li> <li><em>"object_scores"</em>: scores corresponding to each detected object.</li> <li><em>"object_boxes_yxyx"</em>: bounding boxes of the detected objects in the format <em>(ymin, xmin, ymax, xmax).</em></li> </ul> <p> </p> <p><em><strong><sup>†</sup>Note on the cross-modal features: </strong></em>The extracted multi-modal features (ALADIN, CLIPs, CLIP2Video) enable internal searches within the MVK dataset using the query-by-image approach (features can be compared with the dot product). However, to perform searches based on free text, the text needs to be transformed into the joint embedding space according to the specific network being used (see links above). Please be aware that t<strong>he service for transforming text into features is not provided within this repository and should be developed independently using the original feature repositories linked above.</strong></p> <p>We have plans to release the code in the future, allowing the reproduction of the VISIONE system, including the instantiation of all the services to transform text into cross-modal features. However, this work is still in progress, and the code is not currently available.</p> <p> </p> <p><strong>References:</strong></p> <p>[Amato et al. 2023] Amato, G.et al., 2023, January. VISIONE at Video Browser Showdown 2023. In International Conference on Multimedia Modeling (pp. 615-621). Cham: Springer International Publishing.</p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. </p> <p>[Fang H. et al. 2021] Fang H. et al., 2021. Clip2video: Mastering video-text retrieval via image clip. arXiv preprint arXiv:2106.11097.</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. International Journal of Computer Vision, 128(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.</p> <p>[Messina et al. 2022] Messina N. et al., 2022, September. Aladin: distilling fine-grained alignment scores for efficient image-text matching and retrieval. In Proceedings of the 19th International Conference on Content-based Multimedia Indexing (pp. 64-70).</p> <p>[Radford et al. 2021] Radford A. et al., 2021, July. Learning transferable visual models from natural language supervision. In International conference on machine learning (pp. 8748-8763). PMLR.</p> <p>[Schuhmann et al. 2022] Schuhmann C. et al., 2022. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35, pp.25278-25294.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In Proceedings of the IEEE/CV</p>
RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment
<p>This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level.. <br><br>The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops.</p> <p>The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number><br><strong>_1</strong>: Blue<br><strong>_2</strong>: Green<br><strong>_3</strong>: Red<br><strong>_4</strong>: Near Infrared<br><strong>_5</strong>: RedEdge<br><br>Example: An image name <strong>IMG_0200_3 </strong>represents the scene number<strong> 200</strong> in <strong>Red channel</strong></p> <p>This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format.</p> <p>The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article. <br><br>Field experimentation roles:</p> <p>The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.</p> <p>Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'. </p> <p>Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.</p>
Real testing sets for Visual Affordance Segmentation of hand-occluded objects
<p>[<a href="https://arxiv.org/abs/2308.11233">arXiv</a>] [<a href="https://apicis.github.io/projects/acanet.html">webpage</a>] [<a href="https://github.com/SEAlab-unige/acanet">code</a>] [<a href="https://doi.org/10.5281/zenodo.8364197">trained model</a>][<a href="https://doi.org/10.5281/zenodo.5085800">mixed-reality data</a>]</p> <p>RGB images with the corresponding affordance annotation to test affordance segmentation models. Images are selected from two datasets for hand-object pose estimation: <a href="https://www.tugraz.at/institute/icg/research/team-lepetit/research-projects/hand-object-3d-pose-annotation/">HO-3D</a> and <a href="https://corsmal.eecs.qmul.ac.uk/containers_manip.html">CCM</a>.</p> <p>For HO3D we selected 150 frames from the dataset and enriched the annotation of the hand and object segmentation masks with new annotations specific for the affordance segmentation problem.</p> <p>For CCM we selected 150 frames from the dataset and created the annotation specific for the affordance segmentation problem. The forearms and hands in contact with the offered container are annotated. </p> <p>File names are formatted as: <em><videoname>_<framenumber>.png</em></p> <p>Segmentation classes values:</p> <ul> <li> 0: background</li> <li> 1: graspable</li> <li> 2: contain</li> <li> 3: arm</li> </ul> <p> </p> <p><strong>References. </strong></p> <p><strong>Affordance segmentation of hand-occluded containers from exocentric images</strong><br>T. Apicella, A. Xompero, E. Ragusa, R. Berta, A. Cavallaro, P. Gastaldo<br>IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023</p> <pre><code>@inproceedings{apicella2023affordance, title={Affordance segmentation of hand-occluded containers from exocentric images}, author={Apicella, Tommaso and Xompero, Alessio and Ragusa, Edoardo and Berta, Riccardo and Cavallaro, Andrea and Gastaldo, Paolo}, booktitle={IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}, year={2023}, } </code></pre> <p><strong>HOnnotate: A method for 3D Annotation of Hand and Objects Poses<br></strong>S. Hampali, M. Rad, M. Oberweger, V. Lepetit<strong><br></strong>IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020</p> <pre><code>@inproceedings{hampali2020honnotate, title={Honnotate: A method for 3d annotation of hand and object poses}, author={Hampali, Shreyas and Rad, Mahdi and Oberweger, Markus and Lepetit, Vincent}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={3196--3206}, year={2020} }</code></pre> <p><strong>CORSMAL Containers Manipulation (1.0) [Data set]</strong><br>A. Xompero, R. Sanchez-Matilla, R. Mazzon, and A. Cavallaro<br>Queen Mary University of London. <a href="https://doi.org/10.17636/101CORSMAL1"><u>https://doi.org/10.17636/101CORSMAL1</u></a></p> <p> </p> <p><strong>License. </strong>Creative Commons<strong> </strong>Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)</p> <p><strong>Enquiries, Question and Comments. </strong>For enquiries, questions, or comments, please contact <a href="mailto:tommaso.apicella@edu.unige.it">Tommaso Apicella</a>.</p>
List of information objects of Northern Cluster in OneNet project
<p>List of information objects includes in CSV format the names and descriptions of role-to-role data exchanges referred to in the use cases of OneNet Northern cluster.</p> <p>The context of usage of the information objects in business use case and system use cases can be found here: <a href="https://onenet-project.eu/wp-content/uploads/2023/10/D7.2_OneNet_v1.0.pdf">https://onenet-project.eu/wp-content/uploads/2023/10/D7.2_OneNet_v1.0.pdf</a>,<a href="https://onenet-project.eu/wp-content/uploads/2023/05/OneNet_D7.3_v1.0-1.pdf"> https://onenet-project.eu/wp-content/uploads/2023/05/OneNet_D7.3_v1.0-1.pdf</a>,<a href="https://onenet-project.eu/wp-content/uploads/2022/12/OneNet_D7.4_v.1.0.pdf">https://onenet-project.eu/wp-content/uploads/2022/12/OneNet_D7.4_v.1.0.pdf</a></p>
Tuning-less Object Naming with a Foundation Model - Data recorded during testing
<p>We implement a real-time object naming system that enables learning a set of named entities never seen. Our approach employs an existing foundation model that we consider ready to see anything before starting. It turns seen images into relatively small feature vectors that we associate with index to a gradually built vocabulary without any training of fine-tuning of the model. Our contribution is using the association mechanism known from transformers as attention. It has features that support generalization from irrelevant information for distinguishing the entities and potentially enable associating with much more than indices to vocabulary. As a result, the system can work in a one-shot manner and correctly name objects named in different contents. We also outline implementation details of the system modules integrated by a blackboard architecture. Finally, we investigate the<br>system's quality, mainly how many objects it can handle in this way.</p>
THE NATURE OF X-RAYS FROM YOUNG STELLAR OBJECTS IN THE ORION NEBULA CLUSTER - A Chandra HETGS Legacy Project
<p><span>This first release provides the community with a first cut of confusion cleaned X-ray spectra of the Orion Nebula Cluster observed with the HETG onboard the Chandra X-ray Observatory. The data were taken starting in 1999 until 2021. <br><br>The confusion cleaning is based on several aspects of sources for confusion, which includes cluster point sources intersecting with grating dispersions, grating arms intersecting each other in CCD space, as well as grating dispersion overlaps prohibiting proper order sorting. The latter is a major effect and resulted in sometimes severe data losses. In the first release, our automated procedure took care of the vast majority of point sources and grating arm intersections. With respect to the dispersion arm overlaps, in this release we took a statistical approach optimizing the agreement of all four grating dispersion arms in the merged data to agree within a 1 sigma statistical uncertainty over 90% of the bandpass between 2 and 15 Angstrom. For that we used the zero order flux fractions of the interfering sources as the driving parameter. <br><br>There are still many caveats and rooms for improvement, which we will address in upcoming releases, which include the treatment of the increasing background at high dispersion, improve extraction efficiency, exclude observations with non-detections before confusion cleaning, include possible new detections, investigate the 5 A excess we observe in the HEG, though at low statistics, spotcheck individual observations for any residual issues. <br><br>Release 1 provides the community with an excellent starting point for addressing our identified science projects. Out of the 46 sources that were extracted, 37 resulted in valid spectral data. 7 sources have less than 1000 counts in 1st order, some of those may not yet be very useful. <br></span></p> <p> </p> <p>Each directory contains the merged cleaned spectrum and responses for<br>one source. The file "pha2" is a Type II PHA file (multiple spectra)<br>containing the four first order spectra, HEG -1, HEG +1, MEG -1, and<br>MEG +1. Headers have been edited indicate the object (OBJECT), and<br>start and stop times for the set of observations. Since the exposure<br>depends on order, due to the cleaning process, EXPOSURE is a column in<br>the data table. Some other keywords now say "MERGED" since they can<br>vary with observation.</p> <p>There is one effective area file per order (".arf" files). These have<br>also been merged by zeroing out the same regions as excluded in the<br>count spectra, and summed weighting by exposure. They also have<br>similar header edits as for the spectra.</p> <p>While the exposures in headers may say 2 Ms, the actual exposure at<br>any wavelength may be much less. This is not explicitly known, but is<br>implicit in the ignored wavelength regions in the merged counts and<br>responses.</p> <p>There is one grating response matrix (".rmf" files) per order. Since<br>all spectral extractions of all sources used the same cross-dispersion<br>region, there is no change in these files between sources. One set<br>suffices for all extractions. These are in the directory "RMFs", and<br>also for convenience have symbolic links in each source directory.</p> <p>HETG background files have also been provided, one PHA file per first<br>order, in directory HETG_Background. These have been derived from<br>long observations of blank fields. Details are provided in the<br>accompanying memo, hetg_background.pdf.</p> <p>Headers have not been designed for auto-loading of responses (that is<br>CORRFILE, RESPFILE, and BACKFILE are set to 'none').</p>
Figure 27 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 27. Stratigraphical ranges of the lepidocyclinids species at the Cuban sections and their correlation with the American Larger Foraminifera Zones (ABZ) proposed by Mitchel et al. (2022). ABZ zonation scheme calibrated against planktic foraminifera and calcareous nannofossils zonations along with Shallow Benthic Zonation (Mitchell et al., 2022).
Figure 25 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 25. Lepidocyclina ocalana Cushman. Nepionts of megalospheric individuals. A, CA-216-E3(7a); B, CA-216-E3(7b); C, CA-216-E3(12); D, CA-216-F3(3a); E, CA-216-F3(3b); F, CA-216-F3(3c); G, P-559 (513); H, P-559 (509); I, P-562 (491). Blanco Formation (CA-216); Jicotea Formation (P-559; P-562).
Figure 24. A–D, Lepidocyclina ocalana Cushman. A, B in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 24. A–D, Lepidocyclina ocalana Cushman. A, B, equatorial sections of megalospheric individuals; C, D, axial sections of megalospheric individuals. A, P-562(491); B, P-562(481); C, P-562(499); D, P-562(498). Jicotea Formation (P-562).
Figure 23. A–G in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 23. A–G, Lepidocyclina ocalana Cushman. Nepionts of megalospheric individuals. A, CA-216-E3(7a); B, CA-216-E3(7b); C, CA-216-F3(3b); D, P-559 (513); E, CA-216-F3(3a); F,CA-216-E3(12); G, CA-216-F3(3c). Blanco Formation (CA-216); Jicotea Formation (P-559).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.