16 citations · 19 across the 13 of their papers we have counts for
10 papers · 1 filter
Coupled Optimal Transport with Landmark Constraints
Xiang Gu, Jian Sun, Zongben Xu
Existing optimal transport (OT) models primarily seek an OT map or plan between distributions by minimizing a prescribed transport cost or distortion. However, minimizing transport…
Learning Continuous Wasserstein Barycenter Space for Generalized All-in-One Image Restoration
Xiaole Tang, Xiaoyi He, Jiayi Xu +2
Despite substantial advances in all-in-one image restoration for addressing diverse degradations within a unified model, existing methods remain vulnerable to out-of-distribution d…
BaryIR: Learning Multi-Source Unified Representation in Continuous Barycenter Space for Generalizable All-in-One Image Restoration
Xiaole Tang, Xiaoyi He, Xiang Gu +1
Despite remarkable advances made in all-in-one image restoration (AIR) for handling different types of degradations simultaneously, existing methods remain vulnerable to out-of-dis…
GMapLatent: Geometric Mapping in Latent Space
Wei Zeng, Xuebin Chang, Jianghao Su +3
Cross-domain generative models based on encoder-decoder AI architectures have attracted much attention in generating realistic images, where domain alignment is crucial for generat…
Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image Restoration
Xiaole Tang, Xiang Gu, Xiaoyi He +2
All-in-one image restoration has emerged as a practical and promising low-level vision task for real-world applications. In this context, the key issue lies in how to deal with dif…
Prototypical Partial Optimal Transport for Universal Domain Adaptation
Yucheng Yang, Xiang Gu, Jian Sun
Universal domain adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain without requiring the same label sets of both domains. The…
Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration
Xiaole Tang, Xin Hu, Xiang Gu +1
Deep learning-based image restoration methods generally struggle with faithfully preserving the structures of the original image. In this work, we propose a novel Residual-Conditio…
Optimal Transport-Guided Conditional Score-Based Diffusion Models
Xiang Gu, Liwei Yang, Jian Sun +1
Conditional score-based diffusion model (SBDM) is for conditional generation of target data with paired data as condition, and has achieved great success in image translation. Howe…
Generalized Semantic Segmentation by Self-Supervised Source Domain Projection and Multi-Level Contrastive Learning
Liwei Yang, Xiang Gu, Jian Sun
Deep networks trained on the source domain show degraded performance when tested on unseen target domain data. To enhance the model's generalization ability, most existing domain g…
Keypoint-Guided Optimal Transport: Models, Algorithms, and Applications
Xiang Gu, Yucheng Yang, Wei Zeng +2
Existing Optimal Transport (OT) methods mainly derive the optimal transport plan/matching under the criterion of transport cost/distance minimization, which may cause incorrect mat…