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Learning the semantic structure of objects from Web supervision

David Novotny, Diane Larlus, Andrea Vedaldi
While recent research in image understanding has often fo- cused on recognizing more types of objects, understanding more about the objects is just as important. Recognizing object parts and attributes has been extensively studied before, yet learning large space of such concepts remains elusive due to the high cost of providing detailed object annota- tions for supervision. The key contribution of this paper is an algorithm to learn the nameable parts of objects automatically, from images ob- tained by querying Web search engines. The key challenge is the high level of noise in the annotations; to address it, we propose a new uni ed embedding space where the appearance and geometry of objects and their semantic parts are represented uniformly. Geometric relationships are in- duced in a soft manner by a rich set of non-semantic mid-level anchors, bridging the gap between semantic and non-semantic parts.We also show that the resulting embedding provides a visually-intuitive mechanism to navigate the learned concepts and their corresponding images.
ECCV, Amsterdam, The Netherlands, October 11-14, 2016.


2016-028.pdf (6.77 MB)