Domain Adaptation in Computer Vision Applications

<p>1. A Comprehensive Survey on Domain Adaptation for Visual Applications<br/> Gabriela Csurka</p> <p>2. A Deeper Look at Dataset Bias<br/> Tatiana Tommasi, Novi Patricia, Barbara Caputo, and Tinne Tuytelaars</p> <p>Part I: Shallow Domain Adaptation Methods</p> <p>3. Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation<br/> Boqing Gong, Kristen Grauman, and Fei Sha</p> <p>4. Unsupervised Domain Adaptation based on Subspace Alignment<br/> Basura Fernando, Rahaf Aljundi, Rémi Emonet, Amaury Harbard, Marc Sebban, and Tinne Tuytelaars</p> <p>5. Learning Domain Invariant Embeddings by Matching Distributions<br/> Mahsa Baktashmotlagh, Mehrtash Harandi, and Mathieu Salzmann</p> 6. Adaptive Transductive Transfer Machines: A Pipeline for Unsupervised Domain Adaptation<br/> Nazli Farajidavar, Teofilo de Campos, and Josef Kittler<p></p> <p>7. What To Do When the Access to the Source Data is Constrained?<br/> Gabriela Csurka, Boris Chidlovskii, and Stéphane Clinchant</p> <p>Part II: Deep Domain Adaptation Methods</p> <p>8. Correlation Alignment for Unsupervised Domain Adaptation<br/> Baochen Sun, Jiashi Feng, and Kate Saenko</p> <p>9. Simultaneous Deep Transfer Across Domains and Tasks<br/> Judy Hoffman, Eric Tzeng, Trevor Darrell, and Kate Saenko</p> <p>10. Domain-Adversarial Training of Neural Networks<br/> Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky</p> <p>Part III: Beyond Image Classification</p> <p>11. Unsupervised Fisher Vector Adaptation for Re-Identification<br/> Usman Tariq, Jose A. Rodriguez-Serrano, and Florent Perronnin</p> <p>12. Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA<br/> German Ros, Laura Sellart, Gabriel Villalonga, Elias Maidanik, Francisco Molero, Marc Garcia, Adriana Cedeño, Francisco Perez, Didier Ramirez, Eduardo Escobar, Jose Luis Gomez, David Vazquez, and Antonio M. Lopez</p> <p>13. From Virtual to Real World Visual Perception using Domain Adaptation – The DPM as Example<br/> Antonio M. López, Jiaolong Xu, José L. Gómez, David Vázquez, and Germán Ros</p> <p>14. Generalizing Semantic Part Detectors Across Domains<br/> David Novotny, Diane Larlus, and Andrea Vedaldi</p> <p>Part IV: Beyond Domain Adaptation: Unifying Perspectives</p> 15. A Multi-Source Domain Generalization Approach to Visual Attribute Detection<br/> Chuang Gan, Tianbao Yang, and Boqing Gong<p></p> <p>16. Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives<br/> Yongxin Yang and Timothy M. Hospedales</p>