8MVG - Machine Vision Group (Machine Vision Group Department of Electrical and Information Engineering P.O.Box 4500 FI-90014 University of Oulu Finland - Finlande)
Abstract : We study the problem of understanding objects in detail, intended as recognizing a wide array of fine-grained object attributes. To this end, we introduce a dataset of 7,413 airplanes annotated in detail with parts and their attributes, leveraging images donated by airplane spotters and crowd-sourcing both the design and collection of the detailed annotations. We provide a number of insights that should help researchers interested in designing fine-grained datasets for other basic level categories. We show that the collected data can be used to study the relation between part detection and attribute prediction by diagnosing the performance of classifiers that pool information from different parts of an object. We note that the prediction of certain attributes can benefit substantially from accurate part detection. We also show that, differently from previous results in object detection, employing a large number of part templates can improve detection accuracy at the expenses of detection speed. We finally propose a coarse-to-fine approach to speed up detection through a hierarchical cascade algorithm.
Type de document :
Communication dans un congrès
IEEE Conference on Computer Vision and Pattern Recognition, Jun 2014, Columbus, United States. 2014, 〈10.1109/CVPR.2014.463〉
https://hal.inria.fr/hal-00981125
Contributeur : Matthew Blaschko
<>
Soumis le : lundi 21 avril 2014 - 02:53:38
Dernière modification le : vendredi 6 avril 2018 - 13:32:01
Document(s) archivé(s) le : lundi 10 avril 2017 - 15:58:15
Andrea Vedaldi, Siddarth Mahendran, Stavros Tsogkas, Subhransu Maji, Ross B. Girshick, et al.. Understanding Objects in Detail with Fine-grained Attributes. IEEE Conference on Computer Vision and Pattern Recognition, Jun 2014, Columbus, United States. 2014, 〈10.1109/CVPR.2014.463〉. 〈hal-00981125〉
