Prominent Attribute Modification using Attribute Dependent Generative Adversarial Network

Modifying the facial images with desired attributes is important, though challenging tasks in computer vision, where it aims to modify single or multiple attributes of the face image. Some of the existing methods are either based on attribute independent approaches where the modification is done in the latent representation or attribute dependent approaches. The attribute independent methods are limited in performance as they require the desired paired data for changing the desired attributes. Secondly, the attribute independent constraint may result in the loss of information and, hence, fail in generating the required attributes in the face image. In contrast, the attribute dependent approaches are effective as these approaches are capable of modifying the required features along with preserving the information in the given image. However, attribute dependent approaches are sensitive and require a careful model design in generating high-quality results. To address this problem, we propose an attribute dependent face modification approach. The proposed approach is based on two generators and two discriminators that utilize the binary as well as the real representation of the attributes and, in return, generate high-quality attribute modification results. Experiments on the CelebA dataset show that our method effectively performs the multiple attribute editing with preserving other facial details intactly

Face Attribute Modification Using Fine-Tuned Attribute-Modification Network

Multi-domain image-to-image translation with the desired attributes is an important approach for modifying single or multiple attributes of a face image, but is still a challenging task in the computer vision field. Previous methods were based on either attribute-independent or attribute-dependent approaches. The attribute-independent approach, in which the modification is performed in the latent representation, has performance limitations because it requires paired data for changing the desired attributes. In contrast, the attribute-dependent approach is effective because it can modify the required features while maintaining the information in the given image. However, the attribute-dependent approach is sensitive to attribute modifications performed while preserving the face identity, and requires a careful model design for generating high-quality results. To address this problem, we propose a fine-tuned attribute modification network (FTAMN). The FTAMN comprises a single generator and two discriminators. The discriminators use the modified image in two configurations with the binary attributes to fine tune the generator such that the generator can generate high-quality attribute-modification results. Experimental results obtained using the CelebA dataset verify the feasibility and effectiveness of the proposed FTAMN for editing multiple facial attributes while preserving the other details

Depth Estimation From a Single RGB Image Using Fine-Tuned Generative Adversarial Network

Estimating the depth map from a single RGB image is important to understand the nature of the terrain in robot navigation and has attracted considerable attention in the past decade. The existing approaches can accurately estimate the depth from a single RGB image, considering a highly structured environment. The problem becomes more challenging when the terrain is highly dynamic. We propose a fine-tuned generative adversarial network to estimate the depth map effectively for a given single RGB image. The proposed network is composed of a fine-tuned generator and a global discriminator. The encoder part of the generator takes input RGB images and depth maps and generates their joint distribution in the latent space. Subsequently, the decoder part of the generator decodes the depth map from the joint distribution. The discriminator takes real and fake pairs in three different configurations and then guides the generator to estimate the depth map from the given RGB image accordingly. Finally, we conducted extensive experiments with a highly dynamic environment dataset for verifying the effectiveness and feasibility of the proposed approach. The proposed approach could decode the depth map from the joint distribution more effectively and accurately than the existing approaches

Accurate and Consistent Image-to-Image Conditional Adversarial Network

Image-to-image translation based on deep learning has attracted interest in the robotics and vision community because of its potential impact on terrain analysis and image representation, interpretation, modification, and enhancement. Currently, the most successful approach for generating a translated image is a conditional generative adversarial network (cGAN) for training an autoencoder with skip connections. Despite its impressive performance, it has low accuracy and a lack of consistency; further, its training is imbalanced. This paper proposes a balanced training strategy for image-to-image translation, resulting in an accurate and consistent network. The proposed approach uses two generators and a single discriminator. The generators translate images from one domain to another. The discriminator takes the input of three different configurations and guides both the generators to generate realistic images in their corresponding domains while ensuring high accuracy and consistency. Experiments are conducted on different datasets. In particular, the proposed approach outperforms the cGAN in realistic image translation in terms of accuracy and consistency in training