skip to main content


Search for: All records

Creators/Authors contains: "Adkins, Grayson"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. This article presents a semisupervised multilabel fully convolutional network (FCN) for hierarchical object parsing of images. We consider each object part (e.g., eye and head) as a class label and learn to assign every image pixel to multiple coherent part labels. Different from previous methods that consider part labels as independent classes, our method explicitly models the internal relationships between object parts, e.g., that a pixel highly scored for eyes should be highly scored for heads as well. Such relationships directly reflect the structure of the semantic space and thus should be respected while learning the deep representation. We achieve this objective by introducing a multilabel softmax loss function over both labeled and unlabeled images and regularizing it with two pairwise ranking constraints. The first constraint is based on a manifold assumption that image pixels being visually and spatially close to each other should be collaboratively classified as the same part label. The other constraint is used to enforce that no pixel receives significant scores from more than one label that are semantically conflicting with each other. The proposed loss function is differentiable with respect to network parameters and hence can be optimized by standard stochastic gradient methods. We evaluate the proposed method on two public image data sets for hierarchical object parsing and compare it with the alternative parsing methods. Extensive comparisons showed that our method can achieve state-of-the-art performance while using 50% less labeled training samples than the alternatives. 
    more » « less