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CAD 120 affordance dataset

Sawatzky, Johann; Srikantha, Abhilash; Gall, Juergen


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{
  "conceptrecid": "795703", 
  "created": "2017-04-12T13:17:57.791485+00:00", 
  "doi": "10.5281/zenodo.495570", 
  "id": 495570, 
  "links": {
    "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.495570.svg", 
    "doi": "https://doi.org/10.5281/zenodo.495570"
  }, 
  "metadata": {
    "access_right": "open", 
    "access_right_category": "success", 
    "creators": [
      {
        "affiliation": "University of Bonn", 
        "name": "Sawatzky, Johann"
      }, 
      {
        "affiliation": "Carl Zeiss AG", 
        "name": "Srikantha, Abhilash"
      }, 
      {
        "affiliation": "University of Bonn", 
        "name": "Gall, Juergen"
      }
    ], 
    "description": "<p>% ==============================================================================<br>\n% CAD 120 Affordance Dataset<br>\n% Version 1.0<br>\n% ------------------------------------------------------------------------------<br>\n% If you use the dataset please cite:<br>\n%<br>\n% Johann Sawatzky, Abhilash Srikantha, Juergen Gall.<br>\n% Weakly Supervised Affordance Detection.<br>\n% IEEE Conference on Computer Vision and Pattern Recognition (CVPR'17)<br>\n%<br>\n% and<br>\n%<br>\n% H. S. Koppula and A. Saxena.<br>\n% Physically grounded spatio-temporal object affordances.<br>\n% European Conference on Computer Vision (ECCV'14)<br>\n%<br>\n% Any bugs or questions, please email sawatzky AT iai DOT uni-bonn DOT de.<br>\n% ==============================================================================</p>\n\n<p>This is the CAD 120 Affordance Segmentation Dataset based on the Cornell Activity<br>\nDataset CAD 120 (see http://pr.cs.cornell.edu/humanactivities/data.php).</p>\n\n<p>Content</p>\n\n<p>frames/*.png:<br>\nRGB frames selected from Cornell Activity Dataset. To find out the location of the frame<br>\nin the original videos, see video_info.txt.</p>\n\n<p>object_crop_images/*.png<br>\nimage crops taken from the selected frames and resized to 321*321. Each crop is a padded<br>\nbounding box of an object the human interacts with in the video. Due to the padding,<br>\nthe crops may contain background and other objects.<br>\nIn each selected frame, each bounding box was processed. The bounding boxes are already<br>\ngiven in the Cornell Activity Dataset.<br>\nThe 5-digit number gives the frame number, the second number gives the bounding box number<br>\nwithin the frame.</p>\n\n<p>segmentation_mat/*.mat<br>\n321*321*6 segmentation masks for the image crops. Each channel corresponds to an<br>\naffordance (openabe, cuttable, pourable, containable, supportable, holdable, in this order).<br>\nAll pixels belonging to a particular affordance are labeled 1 in the respective channel,<br>\notherwise 0. \u00a0</p>\n\n<p>segmentation_png/*.png<br>\n321*321 png images, each containing the binary mask for one of the affordances.</p>\n\n<p>lists/*.txt<br>\nLists containing the train and test sets for two splits. The actor split ensures that<br>\ntrain and test images stem from different videos with different actors while the object split ensures<br>\nthat train and test data have no (central) object classes in common.<br>\nThe train sets are additionally subdivided into 3 subsets A,B and C. For the actor split,<br>\nthe subsets stem from different videos. For the object split, each subset contains<br>\nevery third crop of the train set.</p>\n\n<p>crop_coordinate_info.txt<br>\nMaps image crops to their coordinates in the frames.</p>\n\n<p>hpose_info.txt<br>\nMaps frames to 2d human pose coordinates. Hand annotated by us.</p>\n\n<p>object_info.txt<br>\nMaps image crops to the (central) object it contains.</p>\n\n<p>visible_affordance_info.txt<br>\nMaps image crops to affordances visible in this crop</p>\n\n<p>\u00a0</p>\n\n<p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55<br>\nThe crops contain the following object classes:<br>\n1.table<br>\n2.kettle<br>\n3.plate<br>\n4.bottle<br>\n5.thermal cup<br>\n6.knife<br>\n7.medicine box<br>\n8.can<br>\n9.microwave<br>\n10.paper box<br>\n11.bowl<br>\n12.mug</p>\n\n<p>Affordances in our set:<br>\n1.openable<br>\n2.cuttable<br>\n3.pourable<br>\n4.containable<br>\n5.supportable<br>\n6.holdable</p>\n\n<p>Note that our object affordance labeling differs from the Cornell Activity Dataset:<br>\nE.g. the cap of a pizza box is considered to be supportable.</p>\n\n<p>\u00a0</p>", 
    "doi": "10.5281/zenodo.495570", 
    "keywords": [
      "computer vision", 
      "affordances", 
      "attributes", 
      "semantic image segmentation", 
      "robotics", 
      "weakly supervised learning", 
      "convolutional neural network", 
      "anticipating human behavior", 
      "mapping on demand"
    ], 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "meeting": {
      "acronym": "CVPR", 
      "dates": "21-26 July 2017", 
      "title": "Computer Vision and Pattern Recognition"
    }, 
    "notes": "Acknowledgments. The work has been financially sup-\nported by the DFG projects GA 1927/5-1 (DFG Research\nUnit FOR 2535 Anticipating Human Behavior) and GA\n1927/2-2 (DFG Research Unit FOR 1505 Mapping on De-\nmand).", 
    "publication_date": "2017-04-07", 
    "references": [
      "Sawatzky, J., Srikantha, A., Gall, J.: Weakly supervised affordance detection.  CVPR (2017)"
    ], 
    "related_identifiers": [
      {
        "identifier": "https://pages.iai.uni-bonn.de/gall_juergen/download/jgall_affordancedetection_cvpr17.pdf", 
        "relation": "isSupplementTo", 
        "scheme": "url"
      }, 
      {
        "identifier": "https://github.com/ykztawas/Weakly-Supervised-Affordance-Detection", 
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        "scheme": "url"
      }
    ], 
    "relations": {
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    "resource_type": {
      "title": "Dataset", 
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    }, 
    "title": "CAD 120 affordance dataset"
  }, 
  "owners": [
    29724
  ], 
  "revision": 3, 
  "updated": "2017-09-08T08:09:00.873163+00:00"
}

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