In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …

Abstract Generative Adversarial Networks (GANs) are a type of deep neural network that
have achieved many state-of-the-art results for generative tasks. GANs can be useful in the …

Deep generative models have achieved conspicuous progress in realistic image synthesis
with multifarious conditional inputs, while generating diverse yet high-fidelity images …

We present a general framework for exemplar-based image translation, which synthesizes a
photo-realistic image from the input in a distinct domain (eg, semantic segmentation mask …

Despite the great success of GANs in images translation with different conditioned inputs
such as semantic segmentation and edge map, generating high-fidelity images with …

We present the full-resolution correspondence learning for cross-domain images, which aids
image translation. We adopt a hierarchical strategy that uses the correspondence from …

The generative adversarial network (GAN) framework has emerged as a powerful tool for
various image and video synthesis tasks, allowing the synthesis of visual content in an …

We present an approach to enhancing the realism of synthetic images. The images are
enhanced by a convolutional network that leverages intermediate representations produced …

Generative Adversarial Networks (GANs) have achieved impressive results in various image
synthesis tasks, and are becoming a hot topic in computer vision research because of the …

Recent deep image-to-image translation techniques allow fast generation of face images
from freehand sketches. However, existing solutions tend to overfit to sketches, thus …