Domain Generalization and Adaptation Using Low Rank Exemplar SVMs

By Wen Li, Zheng Xu, Dong Xu, Dengxin Dai, Luc Van Gool. 2018

125 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Luc Van Gool
2. Ziwei Liu
3. Eric P. Xing
4. Dengxin Dai
5. Wen Li
6. Barbara Caputo
7. Dahua Lin
8. Jiashi Feng

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. Trevor Darrell
4. Dacheng Tao
5. Chen Change Loy
6. Xiaogang Wang
7. Jitendra Malik
8. Andrew Zisserman

In Representation & Self-Supervised Learning

1. Wen Li
2. Luc Van Gool
3. Haohan Wang
4. Eric P. Xing
5. Ziwei Liu
6. Barbara Caputo
7. Mathieu Salzmann
8. Yuhua Chen

Citations in the corpus

• Learning Robust Global Representations by Penalizing Local Predictive Power (2019, methodology): “We test on the PACS data set (Li et al., 2017a), which consists of collections of images over four domains, including photo, art painting, cartoon, and sketch.”
• Learning Robust Representations by Projecting Superficial Statistics Out (2018, methodology): “Finally, we tested on the PACS data set (Li et al., 2017a), which consists of collections of images of seven different objects over four domains, including photo, art painting, cartoon, and sketch.”
• Domain Adaptive Faster R-CNN for Object Detection in the Wild (2018, background): “Domain Adaptation: Domain adaptation has been widely studied for image classification in computer vision [10, 11, 33, 23, 22, 14, 52, 40, 15, 18, 50, 45, 43, 35].”
• Open Compound Domain Adaptation (2019, background): “Though domain generalization [24, 22] and latent domain adaptation [16,”
• ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes (2017, background): “In computer vision, domain adaptation has been widely studied as an image classification problem in computer vision [20, 12, 10, 7, 40, 26, 8, 9, 36, 30, 29, 23].”
• DLOW: Domain Flow for Adaptation and Generalization (2018, background): “Domain generalization is a similar problem, which aims to learn a model that could be generalized to an unseen target domain by using multiple labeled source domains [42, 15, 45, 41, 44, 34, 36, 35].”
• Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach (2018, background): “It is mostly studied in image recognition problems by both conventional approaches [29, 18, 15, 10, 33] and CNN-based approaches [36, 12, 14, 50, 42, 39, 31, 19, 37, 38].”
• Beyond Sharing Weights for Deep Domain Adaptation (2016, methodology): “A different SVMbased technique was proposed in [33].”
• Best Sources Forward: Domain Generalization through Source-Specific Nets (2018, methodology): “This idea is exploited by previous methods searching for a domain invariant feature space where to project the data [5, 16] or by approaches attempting to generate domain agnostic classifiers using both shallow [18, 6] or deep learning models [10, 15, 12].”