103 citations · 427 across the 26 of their papers we have counts for
28 papers
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering
Shiqi Yang, Yaxing Wang, Joost van de Weijer +3
Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible…
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling
Dixian Zhu, Bokun Wang, Zhi Chen +4
This paper considers a novel application of deep AUC maximization (DAM) for multi-instance learning (MIL), in which a single class label is assigned to a bag of instances (e.g., mu…
3D-Aware Multi-Class Image-to-Image Translation with NeRFs
Senmao Li, Joost van de Weijer, Yaxing Wang +3
Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aw…
Adaptive Texture Filtering for Single-Domain Generalized Segmentation
Xinhui Li, Mingjia Li, Yaxing Wang +2
Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features. Existing methods diversif…
StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing
Senmao Li, Joost van de Weijer, Taihang Hu +5
A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images.They either finetune the model, or invert the…
OneRing: A Simple Method for Source-free Open-partial Domain Adaptation
Shiqi Yang, Yaxing Wang, Kai Wang +2
In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-OPDA), which addresses the situation where there exist both domain and category shifts between source a…
MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation
Aitor Alvarez-Gila, Joost van de Weijer, Yaxing Wang +1
We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera loca…
Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation
Shiqi Yang, Yaxing Wang, Kai Wang +2
We propose a simple but effective source-free domain adaptation (SFDA) method. Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors…
Transferring Unconditional to Conditional GANs with Hyper-Modulation
Héctor Laria, Yaxing Wang, Joost van de Weijer +1
GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-qu…
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer +2
Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible…
A Novel Framework for Image-to-image Translation and Image Compression
Fei Yang, Yaxing Wang, Luis Herranz +2
Data-driven paradigms using machine learning are becoming ubiquitous in image processing and communications. In particular, image-to-image (I2I) translation is a generic and widely…
Generalized Source-free Domain Adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer +2
Domain adaptation (DA) aims to transfer the knowledge learned from a source domain to an unlabeled target domain. Some recent works tackle source-free domain adaptation (SFDA) wher…
TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets
Yaxing Wang, Hector Laria Mantecon, Joost van de Weijer +2
Image-to-image (I2I) translation has matured in recent years and is able to generate high-quality realistic images. However, despite current success, it still faces important chall…
MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains
Yaxing Wang, Abel Gonzalez-Garcia, Chenshen Wu +4
GANs largely increases the potential impact of generative models. Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that i…
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs
Yaxing Wang, Lu Yu, Joost van de Weijer
Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require lar…
Casting a BAIT for Offline and Online Source-free Domain Adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer +2
We address the source-free domain adaptation (SFDA) problem, where only the source model is available during adaptation to the target domain. We consider two settings: the offline…
Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning
Hui Xie, Zhe Pan, Leixin Zhou +5
Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary and mutual influence between adjacent ob…
Semi-supervised Learning for Few-shot Image-to-Image Translation
Yaxing Wang, Salman Khan, Abel Gonzalez-Garcia +2
In the last few years, unpaired image-to-image translation has witnessed remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely…
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
Lei Kang, Pau Riba, Yaxing Wang +3
Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrar…
MineGAN: effective knowledge transfer from GANs to target domains with few images
Yaxing Wang, Abel Gonzalez-Garcia, David Berga +3
One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality netw…
SDIT: Scalable and Diverse Cross-domain Image Translation
Yaxing Wang, Abel Gonzalez-Garcia, Joost van de Weijer +1
Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfe…
Controlling biases and diversity in diverse image-to-image translation
Yaxing Wang, Abel Gonzalez-Garcia, Joost van de Weijer +1
The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT),…
Mix and match networks: cross-modal alignment for zero-pair image-to-image translation
Yaxing Wang, Luis Herranz, Joost van de Weijer
This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities. We assume that only some of the pairwise translations have…
Memory Replay GANs: learning to generate images from new categories without forgetting
Chenshen Wu, Luis Herranz, Xialei Liu +3
Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investi…