We find that the addition of a small amount of private data greatly improves the performance of our model, which highlights the limitations of using synthetic data to train machine learning models.
Recently, generative adversarial networks (GANs) have achieved stunning realism, fooling even human observers.
We address the problem of 3D object detection from 2D monocular images in autonomous driving scenarios.
Using this methodology, this paper shows that overfitting is not detectable in the pure GAN models proposed in the literature, in contrast with those using hybrid adversarial losses, which are amongst the most widely applied generative methods.
This strategy opens the door to the use of PBM in new applications for which the notion of image categories is irrelevant, such as instance-based image retrieval, for example.
Part-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built.
The challenge addressed in this paper is to design a common universal representation such that a single merged signature is transmitted to the server, whatever be the type and number of features computed by the client, ensuring nonetheless an optimal performance.
The experiments clearly demonstrate the scalability and improved performance of the proposed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.
We propose a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features.
A second variant of our approach that includes the fully connected DCNN layers significantly outperforms Fisher vector schemes and performs comparably to DCNN approaches adapted to Pascal VOC 2007, yet at only a small fraction of the training and testing cost.
This paper introduces a novel image representation capturing feature dependencies through the mining of meaningful combinations of visual features.
We propose a new model for recognizing human attributes (e. g. wearing a suit, sitting, short hair) and actions (e. g. running, riding a horse) in still images.