Gaze latent support vector machine for image classification
This paper deals with image categorization from weak supervision, e.g. global image labels. We propose to improve the region selection performed in latent variable models such as Latent Support Vector Machine (LSVM) by leveraging human eye movement features collected from an eye-tracker device. We introduce a new model, Gaze Latent Support Vector Machine (G-LSVM), whose region selection during training is biased toward regions with a large gaze density ratio. On this purpose, the training objective is enriched with a gaze loss, from which we derive a convex upper bound, leading to a Concave-Convex Procedure (CCCP) optimization scheme. Experiments show that G-LSVM significantly outperforms LSVM in both object detection and action recognition problems on PASCAL VOC 2012. We also show that our G-LSVM is even slightly better than a model trained from bounding box annotations, while gaze labels are much cheaper to collect.