116 citations · 384 across the 16 of their papers we have counts for
18 papers
Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space
Yahui Liu, Bin Tian, Yisheng Lv +2
Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention, bu…
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…
Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation
Jichao Zhang, Aliaksandr Siarohin, Yahui Liu +3
Generative Neural Radiance Fields (GNeRF)-based 3D-aware GANs have showcased remarkable prowess in crafting high-fidelity images while upholding robust 3D consistency, particularly…
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,…
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers
Bin Ren, Yahui Liu, Yue Song +4
Position Embeddings (PEs), an arguably indispensable component in Vision Transformers (ViTs), have been shown to improve the performance of ViTs on many vision tasks. However, PEs…
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…
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…
Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images
Lei Ding, Hao Tang, Yahui Liu +3
Building extraction in VHR RSIs remains a challenging task due to occlusion and boundary ambiguity problems. Although conventional convolutional neural networks (CNNs) based method…
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…
Pairwise Interactive Graph Attention Network for Context-Aware Recommendation
Yahui Liu, Furao Shen, Jian Zhao
Context-aware recommender systems (CARS), which consider rich side information to improve recommendation performance, have caught more and more attention in both academia and indus…
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…
Fast 3D Line Segment Detection From Unorganized Point Cloud
Xiaohu Lu, Yahui Liu, Kai Li
This paper presents a very simple but efficient algorithm for 3D line segment detection from large scale unorganized point cloud. Unlike traditional methods which usually extract 3…
Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network
Yahui Liu, Jian Yao, Li Li +2
We develop a novel deep contour detection algorithm with a top-down fully convolutional encoder-decoder network. Our proposed method, named TD-CEDN, solves two important issues in…