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The head pose is an important cue for computer vision. Traditionally considered in human computer interaction applications,
it becomes very hard to model in surveillance scenarios, due to the tiny head size. Additionally, no public dataset contains continuous head pose annotations in open scenery, making the challenge even harder to face. Here we present a
framework based on Faster RCNN, which introduces a branch in the network architecture related to the head pose estimation.

The multiple types of social media data have abundant information, but learning multi-modal social data is challenging due to data heterogeneity and noise in user-generated data. To address this problem, we propose a multi-view network-based clustering approach that is robust to noise and fully reflects the underlying structure of the comprehensive network. To demonstrate the proposed approach, we experimented with clustering challenging tagged images of landmarks.

Over recent years there have been several efforts which aim to standardise a royalty-free video codec, such as Thor developed by Cisco, and AV1 developed by the Alliance for Open Media. In this paper we discuss how some compression tools in Thor were integrated into the emerging AV1 codec aiming to increase compression efficiency as well as to decrease computational complexity.

Eyes represent the most distinctive features of the human face, while their position and movements are a significant source of information about the cognitive and affective state of humans. Precise eye center localization constitutes a challenging problem in many human-computer interaction applications. In this work, an automatic, non-intrusive method is introduced for the precise eye center localization,based on a modified version of the Fast Radial Symmetry Transform.

In recent years, the triplet loss-based deep neural networks (DNN) are widely used in the task of face recognition and achieve the state-of-the-art performance. However, the complexity of training the triplet loss-based DNN is significantly high due to the difficulty in generating high-quality training samples. In this paper, we propose a novel DNN training framework to accelerate the training process of the triplet loss-based DNN and meanwhile to improve the performance of face recognition. More specifically, the proposed framework contains two stages: 1) The DNN initialization.

In this paper, we propose a novel approach for saliency detection for seismic applications using 3D-FFT local spectra and multi-dimensional plane projections. We develop a projection scheme by dividing a 3D-FFT local spectrum of a data volume into three distinct components, each depicting changes along a different dimension of the data. The saliency detection results obtained using each projected component are then combined to yield a saliency map.

In this paper, we propose a novel approach for saliency detection for seismic applications using 3D-FFT local spectra and multi-dimensional plane projections. We develop a projection scheme by dividing a 3D-FFT local spectrum of a data volume into three distinct components, each depicting changes along a different dimension of the data. The saliency detection results obtained using each projected component are then combined to yield a saliency map.

Unsupervised feature selection has been proven to be an efficient technique in mitigating the curse of dimensionality. It helps to understand and analyze the prevalent high-dimensional unlabeled data. Recently, based on the self-similarity property of objects, self-representation which assumes that a feature can be represented by the linear combination of its relevant features has been successfully used in unsupervised feature selection. In this paper, we propose a novel algorithm termed Manifold Regularized Selfrepresentation(MRSR) based on the self-representation ability of features.

Fully connected multi layer neural networks such as Deep Boltzmann Machines (DBM) performs better than fully connected single layer neural networks in image classification tasks and has a smaller number of hidden layer neurons than Extreme Learning Machine (ELM) based fully connected multi layer neural networks such as Multi Layer ELM (ML-ELM) and Hierarchical ELM (H-ELM) However, ML-ELM and H-ELM has a smaller training time than DBM.