Asking Friendly Strangers: Non-Semantic Attribute Transfer

Nickisch, and Harmeling 2009; Parikh and Grauman We propose an attention-guided transfer network. Briefly, 2011; Akata et al. 2013), learn object models expediently our approach works as follows. First, the network receives by providing information about multiple object classes training images for attributes in both the source and target with each attribute label (Kovashka, Vijayanarasimhan, and domains. Second, it separately learns models for the attributes Grauman 2011; Parkash and Parikh 2012), interactively recognize in each domain, and then measures how related each fine-grained object categories (Branson et al. 2010; target domain classifier is to the classifiers in the source domains. Wah and Belongie 2013), and learn to retrieve images from Finally, it uses these measures of similarity (relatedness) precise human feedback (Kumar et al. 2011; Kovashka, to compute a weighted combination of the source classifiers, Parikh, and Grauman 2015). Recent ConvNet approaches which then becomes the new classifier for the target have shown how to learn accurate attribute models through attribute. We develop two methods, one where the target and multi-task learning (Fouhey, Gupta, and Zisserman 2016; source domains are disjoint, and another where there is some Huang et al. 2015) or by localizing attributes (Xiao and overlap between them. Importantly, we show that when the Jae Lee 2015; Singh and Lee 2016). However, deep learning source attributes come from a diverse set of domains, the with ConvNets requires a large amount of data to be available gain we obtain from this transfer of knowledge is greater for the task of interest, or for a related task (Oquab et than if only use attributes from the same domain.