559 citations · 609 across the 9 of their papers we have counts for
9 papers
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation
Bingyu Liu, Yuhong Guo, Jieping Ye +1
Recent domain adaptation methods have demonstrated impressive improvement on unsupervised domain adaptation problems. However, in the semi-supervised domain adaptation (SSDA) setti…
Domain Adaptation with Incomplete Target Domains
Zhenpeng Li, Jianan Jiang, Yuhong Guo +3
Domain adaptation, as a task of reducing the annotation cost in a target domain by exploiting the existing labeled data in an auxiliary source domain, has received a lot of attenti…
Bi-Dimensional Feature Alignment for Cross-Domain Object Detection
Zhen Zhao, Yuhong Guo, Jieping Ye
Recently the problem of cross-domain object detection has started drawing attention in the computer vision community. In this paper, we propose a novel unsupervised cross-domain de…
Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data
Zhen Zhao, Bingyu Liu, Yuhong Guo +1
In this paper, we present our proposed ensemble model with batch spectral regularization and data blending mechanisms for the Track 2 problem of the cross-domain few-shot learning…
A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning
Jianan Jiang, Zhenpeng Li, Yuhong Guo +1
In this paper, we present a new method, Transductive Multi-Head Few-Shot learning (TMHFS), to address the Cross-Domain Few-Shot Learning (CD-FSL) challenge. The TMHFS method extend…
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification
Bingyu Liu, Zhen Zhao, Zhenpeng Li +3
In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge. Specifically, we pro…
Mutual Learning Network for Multi-Source Domain Adaptation
Zhenpeng Li, Zhen Zhao, Yuhong Guo +2
Early Unsupervised Domain Adaptation (UDA) methods have mostly assumed the setting of a single source domain, where all the labeled source data come from the same distribution. How…
Adaptive Object Detection with Dual Multi-Label Prediction
Zhen Zhao, Yuhong Guo, Haifeng Shen +1
In this paper, we propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection by exploiting multi-label object recognition as a dual auxiliar…
Object Detection in 20 Years: A Survey
Zhengxia Zou, Keyan Chen, Zhenwei Shi +2
Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Over the past two decades, we have seen…