Identity-preserving Editing of Multiple Facial Attributes by Learning Global Edit Directions and Local Adjustments

Semantic facial attribute editing using pre-trained Generative Adversarial Networks (GANs) has attracted a great deal of attention and effort from researchers in recent years. Due to the high quality of face images generated by StyleGANs, much work has focused on the StyleGANs' latent space and the proposed methods for facial image editing. Although these methods have achieved satisfying results for manipulating user-intended attributes, they have not fulfilled the goal of preserving the identity, which is an important challenge. We present ID-Style, a new architecture capable of addressing the problem of identity loss during attribute manipulation. The key components of ID-Style include Learnable Global Direction (LGD), which finds a shared and semi-sparse direction for each attribute, and an Instance-Aware Intensity Predictor (IAIP) network, which finetunes the global direction according to the input instance. Furthermore, we introduce two losses during training to enforce the LGD to find semi-sparse semantic directions, which along with the IAIP, preserve the identity of the input instance. Despite reducing the size of the network by roughly 95% as compared to similar state-of-the-art works, it outperforms baselines by 10% and 7% in Identity preserving metric (FRS) and average accuracy of manipulation (mACC), respectively

LIP-READING VIA DEEP NEURAL NETWORKS USING HYBRID VISUAL FEATURES

Lip-reading is typically known as visually interpreting the speaker's lip movements during speaking. Experiments over many years have revealed that speech intelligibility increases if visual facial information becomes available. This effect becomes more apparent in noisy environments. Taking steps toward automating this process, some challenges will be raised such as coarticulation phenomenon, visual units' type, features diversity and their inter-speaker dependency. While efforts have been made to overcome these challenges, presentation of a flawless lip-reading system is still under the investigations. This paper searches for a lipreading model with an efficiently developed incorporation and arrangement of processing blocks to extract highly discriminative visual features. Here, application of a properly structured Deep Belief Network (DBN)- based recognizer is highlighted. Multi-speaker (MS) and speaker-independent (SI) tasks are performed over CUAVE database, and phone recognition rates (PRRs) of 77.65% and 73.40% are achieved, respectively. The best word recognition rates (WRRs) achieved in the tasks of MS and SI are 80.25% and 76.91%, respectively. Resulted accuracies demonstrate that the proposed method outperforms the conventional Hidden Markov Model (HMM) and competes well with the state-of-the-art visual speech recognition works

Gaussian-valued particle swarm optimization

This paper examines the position update equation of the particle swarm optimization (PSO) algorithm, leading to the proposal of a simplified position update based upon a Gaussian distribution. The proposed algorithm, Gaussian-valued particle swarm optimization (GVPSO), generates probabilistic positions by retaining key elements of the canonical update procedure while also removing the need to specify values for the traditional PSO control parameters. Experimental results across a set of 60 benchmark problems indicate that GVPSO outperforms both the standard PSO and the bare bones particle swarm optimization (BBPSO) algorithm, which also employs a Gaussian distribution to generate particle positions.The National Research Foundation (NRF) of South Africa (Grant Number 46712) and the Natural Sciences and Engineering Research Council of Canada (NSERC).http://link.springer.combookseries/5582019-10-03hj2018Computer Scienc

An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multi-population artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC. Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results