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mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)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 may belong to different domains, have partial annotations, and/or have different data modalities. This paper formulates this as a multi-source domain adaptation and label unification problem, and proposes a novel method for it. Our method consists of a ...Conference Paper -
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)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 advantage of improved generalization to unseen domains. In this work, we propose a principled meta-learning based approach to OCDA for semantic segmentation, MOCDA, by modeling the unlabeled target domain continuously. Our approach consists of four key steps. First, ...Conference Paper -
Fast algorithms for linear and kernel SVM+
(2016)Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016)Conference Paper -
Deep Domain Adaptation by Geodesic Distance Minimization
(2017)Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops (ICCVW 2017)Conference Paper -
Domain Adaptive Faster R-CNN for Object Detection in the Wild
(2018)2018 IEEE/CVF Conference on Computer Vision and Pattern RecognitionConference Paper -
Analogical Image Translation for Fog Generation
(2021)Proceedings of the AAAI Conference on Artificial IntelligenceImage-to-image translation is to map images from a given style to another given style. While exceptionally successful, current methods assume the availability of training images in both source and target domains, which does not always hold in practice. Inspired by humans' reasoning capability of analogy, we propose analogical image translation (AIT) that exploit the concept of gist, for the first time. Given images of two styles in the ...Conference Paper