313 citations · 474 across the 22 of their papers we have counts for
8 papers · 1 filter
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
Rui Gong, Yuhua Chen, Danda Pani Paudel +5
Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advant…
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
Rui Gong, Dengxin Dai, Yuhua Chen +2
One challenge of object recognition is to generalize to new domains, to more classes and/or to new modalities. This necessitates methods to combine and reuse existing datasets that…
Region Comparison Network for Interpretable Few-shot Image Classification
Zhiyu Xue, Lixin Duan, Wen Li +2
While deep learning has been successfully applied to many real-world computer vision tasks, training robust classifiers usually requires a large amount of well-labeled data. Howeve…
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search
Yuan Tian, Qin Wang, Zhiwu Huang +5
In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) ar…
Analogical Image Translation for Fog Generation
Rui Gong, Dengxin Dai, Yuhua Chen +2
Image-to-image translation is to map images from a given \emph{style} to another given \emph{style}. While exceptionally successful, current methods assume the availability of trai…
The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures
Yawei Li, Wen Li, Martin Danelljan +4
In this paper, we tackle the problem of convolutional neural network design. Instead of focusing on the design of the overall architecture, we investigate a design space that is us…
Deeply Aligned Adaptation for Cross-domain Object Detection
Minghao Fu, Zhenshan Xie, Wen Li +1
Cross-domain object detection has recently attracted more and more attention for real-world applications, since it helps build robust detectors adapting well to new environments. I…
Unbiased Mean Teacher for Cross-domain Object Detection
Jinhong Deng, Wen Li, Yuhua Chen +1
Cross-domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift between two distincti…