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We address the question of what visual cues, including scene objects and demographic attributes, contribute to the automatic inference of perceived ambiance in social media venues. We first use a stateof- art, deep scene semantic parsing method and a face ...
The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical images, and requires more precise localization. To address these challenges, we ...
The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a dense CRF can be minimized accurately using a linear programming (LP) relaxat ...
In this article, we review recent uses of convolutional neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding performance on object clas ...
This thesis addresses the problem of recovering the 3-D shape of a deformable object in single images, or image sequences acquired by a monocular video camera, given that a 3-D template shape and a template image of the object are available. While being a ...
We address the problem of modeling complex target behavior using a stochastic model that integrates object dynamics, statistics gathered from the environment and semantic knowledge about the scene. The method exploits prior knowledge to build point-wise po ...
Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typical ...
The work presented in this dissertation lies in the domains of image classification, object detection, and machine learning. Whether it is training image classifiers or object detectors, the learning phase consists in finding an optimal boundary between po ...
The work presented in this dissertation lies in the domains of image classification, object detection, and machine learning. Whether it is training image classifiers or object detectors, the learning phase consists in finding an optimal boundary between po ...
This paper proposes a novel approach to improve speaker modeling using knowledge transferred from face representation. In particular, we are interested in learning a discriminative metric which allows speaker turns to be compared directly, which is benefic ...
