173 citations · 428 across the 18 of their papers we have counts for
11 papers · 1 filter
Smooth image-to-image translations with latent space interpolations
Yahui Liu, Enver Sangineto, Yajing Chen +5
Multi-domain image-to-image (I2I) translations can transform a source image according to the style of a target domain. One important, desired characteristic of these transformation…
Spatial Entropy as an Inductive Bias for Vision Transformers
Elia Peruzzo, Enver Sangineto, Yahui Liu +4
Recent work on Vision Transformers (VTs) showed that introducing a local inductive bias in the VT architecture helps reducing the number of samples necessary for training. However,…
ISF-GAN: An Implicit Style Function for High-Resolution Image-to-Image Translation
Yahui Liu, Yajing Chen, Linchao Bao +3
Recently, there has been an increasing interest in image editing methods that employ pre-trained unconditional image generators (e.g., StyleGAN). However, applying these methods to…
Click to Move: Controlling Video Generation with Sparse Motion
Pierfrancesco Ardino, Marco De Nadai, Bruno Lepri +2
This paper introduces Click to Move (C2M), a novel framework for video generation where the user can control the motion of the synthesized video through mouse clicks specifying sim…
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…
Efficient Training of Visual Transformers with Small Datasets
Yahui Liu, Enver Sangineto, Wei Bi +3
Visual Transformers (VTs) are emerging as an architectural paradigm alternative to Convolutional networks (CNNs). Differently from CNNs, VTs can capture global relations between im…
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…
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…
Gesture-to-Gesture Translation in the Wild via Category-Independent Conditional Maps
Yahui Liu, Marco De Nadai, Gloria Zen +2
Recent works have shown Generative Adversarial Networks (GANs) to be particularly effective in image-to-image translations. However, in tasks such as body pose and hand gesture tra…