Iman Abbasnejad

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In this paper we tackle the problem of efficient video event detection. We argue that linear detection functions should be preferred in this regard due to their scalability and efficiency during estimation and evaluation. A popular approach in this regard is to represent a sequence using a bag of words (BOW) representation due to its: (i) fixed(More)
In this paper the problem of complex event detection is addressed. Existing event detection methods are limited to features that are extracted from local spatial or spatio-temporal patches from the videos. However, this makes the model more vulnerable to the events that have similar concepts with different actions e.g. "Open drawer" and "Open cupboard".(More)
Compressed sensing is a simple and efficient technique that has a number of applications in signal processing and machine learning. In machine learning it provides answers to questions such as: "under what conditions is the sparse representation of data efficient?", "when is learning a large margin classifier directly on the compressed(More)
In this paper, we tackle the problem of face classification and verification. We present a novel face representation method based on a Bayesian network. The model captures dependencies between 2D salient facial regions and the full 3D geometrical model of the face, which makes it robust to pose variations, and useable in unconstrained environments. We(More)
Pose recognition has recently become a very hot research topic in computer vision and multimedia information processing. In this paper, we propose a generative model for pose recognition based on mixtures of the exponential family of distributions. The distributions which are considered in this paper are the Multivariate Gaussian (MG), Rayleigh (R), Poisson(More)
Real-time outdoor navigation in highly dynamic environments is an crucial problem. The recent literature on real-time static SLAM don't scale up to dynamic outdoor environments. Most of these methods assume moving objects as outliers or discard the information provided by them. We propose an algorithm to jointly infer the camera trajectory and the moving(More)
In this paper the problem of complex event detection in the continuous domain (i.e. events with unknown starting and ending locations) is addressed. Existing event detection methods are limited to features that are extracted from the local spatial or spatio-temporal patches from the videos. However, this makes the model vulnerable to the events with similar(More)
Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input z to a sample x that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function to transform a deterministic input y′ to a sample(More)
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