175 citations · 1.2k across the 65 of their papers we have counts for
6 papers · 1 filter
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping
Huan Fu, Mingming Gong, Chaohui Wang +3
Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples. Finding the optimal GXY without paired data is…
Domain Generalization via Conditional Invariant Representation
Ya Li, Mingming Gong, Xinmei Tian +2
Domain generalization aims to apply knowledge gained from multiple labeled source domains to unseen target domains. The main difficulty comes from the dataset bias: training data a…
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector
Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh +3
We propose an attention-based method that aggregates local image features to a subject-level representation for predicting disease severity. In contrast to classical deep learning…
MoE-SPNet: A Mixture-of-Experts Scene Parsing Network
Huan Fu, Mingming Gong, Chaohui Wang +1
Scene parsing is an indispensable component in understanding the semantics within a scene. Traditional methods rely on handcrafted local features and probabilistic graphical models…
Deep Ordinal Regression Network for Monocular Depth Estimation
Huan Fu, Mingming Gong, Chaohui Wang +2
Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by exploring…
Causal Generative Domain Adaptation Networks
Mingming Gong, Kun Zhang, Biwei Huang +3
An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adap…