English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Most of the approaches for discovering visual attributes in images demandsignificant supervision, which is cumbersome to obtain. In this paper, we aimto discover visual attributes in a weakly supervised setting that is commonlyencountered with contemporary image search engines. Deep Convolutional NeuralNetworks (CNNs) have enjoyed remarkable success in vision applicationsrecently. However, in a weakly supervised scenario, widely used CNN trainingprocedures do not learn a robust model for predicting multiple attribute labelssimultaneously. The primary reason is that the attributes highly co-occurwithin the training data. To ameliorate this limitation, we proposeDeep-Carving, a novel training procedure with CNNs, that helps the netefficiently carve itself for the task of multiple attribute prediction. Duringtraining, the responses of the feature maps are exploited in an ingenious wayto provide the net with multiple pseudo-labels (for training images) forsubsequent iterations. The process is repeated periodically after a fixednumber of iterations, and enables the net carve itself iteratively forefficiently disentangling features. Additionally, we contribute anoun-adjective pairing inspired Natural Scenes Attributes Dataset to theresearch community, CAMIT - NSAD, containing a number of co-occurringattributes within a noun category. We describe, in detail, salient aspects ofthis dataset. Our experiments on CAMIT-NSAD and the SUN Attributes Dataset,with weak supervision, clearly demonstrate that the Deep-Carved CNNsconsistently achieve considerable improvement in the precision of attributeprediction over popular baseline methods.

DEEP-CARVING: Discovering Visual Attributes by Carving Deep Neural Nets