Auglabel: Exploiting Word Representations to Augment Labels for Face Attribute Classification

Abstract

Augmenting data in image space (eg. flipping, cropping etc) and activation space (eg. dropout) are being widely used to regularise deep neural networks and have been successfully applied on several computer vision tasks. Unlike previous works, which are mostly focused on doing augmentation in the aforementioned domains, we propose to do augmentation in label space. In this paper, we present a novel method to generate fixed dimensional labels with continuous values for images by exploiting the word2vec representations of the existing categorical labels. We then append these representations with existing categorical labels and train the model. We validated our idea on two challenging face attribute classification data sets viz. CelebA and LFWA. Our extensive experiments show that the augmented labels improve the performance of the competitive deep learning baseline and reduce the need of annotated real data up to 50%, while attaining a performance similar to the state-of-the-art methods.

Inducing Optimal Attribute Representations for Conditional GANs

“…As we know, word2vec are learned from the large corpus and bears syntactic and semantic relationships between the words [30]. These are also being useful for attributes classification [2]. Attrbs-weights are equipped with higher-order visual information including the spatial location of the attributes (See Fig.…”

Fair Facial Attribute Classification via Causal Graph-Based Attribute Translation

“…Facial attribute classification detects the presence versus absence of various labeled attributes, including bio-metric features (“big nose”), expression (“smiling”), and worn accessories (“glasses”). Attribute classification supports various tasks including tagging, searching, detecting, and verification of identity [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Existing facial attribute classification algorithms have solely been focused on prediction accuracy and are highly likely to suffer from prediction bias that leads to disparity in performance among various population subgroups belonging to different gender, race and age.…”