61 citations · 80 across the 6 of their papers we have counts for
10 papers
Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation
Yahui Liu, Enver Sangineto, Yajing Chen +6
Image-to-Image (I2I) multi-domain translation models are usually evaluated also using the quality of their semantic interpolation results. However, state-of-the-art models frequent…
Semantic-Guided Inpainting Network for Complex Urban Scenes Manipulation
Pierfrancesco Ardino, Yahui Liu, Elisa Ricci +2
Manipulating images of complex scenes to reconstruct, insert and/or remove specific object instances is a challenging task. Complex scenes contain multiple semantics and objects, w…
Retrieval Guided Unsupervised Multi-domain Image-to-Image Translation
Raul Gomez, Yahui Liu, Marco De Nadai +3
Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into…
Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach
Yahui Liu, Marco De Nadai, Deng Cai +4
Manipulating visual attributes of images through human-written text is a very challenging task. On the one hand, models have to learn the manipulation without the ground truth of t…
Vanishing Point Guided Natural Image Stitching
Kai Chen, Jian Yao, Jingmin Tu +3
Recently, works on improving the naturalness of stitching images gain more and more extensive attention. Previous methods suffer the failures of severe projective distortion and un…
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling
Yahui Liu, Marco De Nadai, Jian Yao +3
Unsupervised image-to-image translation (UNIT) aims at learning a mapping between several visual domains by using unpaired training images. Recent studies have shown remarkable suc…