Subspace Alignment Based Domain Adaptation for RCNN Detector

Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2), 303-338

2010

Earlier work this paper cites.

Felzenszwalb, Pedro F., et al. ”Object detection with discriminatively trained part-based models.” Pattern Analysis and Machine Intelligence, IEEE Transactions on 32.9 (2010): 1627-1645

2010

Earlier work this paper cites.

Gopalan, Raghuraman, Ruonan Li, and Rama Chellappa. ”Domain adaptation for object recognition: An unsupervised approach.” In Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 999-1006. IEEE, 2011

2011

Earlier work this paper cites.

Wang, Chang, and Sridhar Mahadevan. ”Heterogeneous domain adaptation using manifold alignment.” IJCAI Proceedings-International Joint Conference on Artificial Intelligence. Vol. 22. No. 1. 2011

2011

Earlier work this paper cites.

Pan, S. J., Tsang, I. W., Kwok, J. T., & Yang, Q. (2011). Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2), 199-210

2011

Earlier work this paper cites.

A. Torralba, A. Efros. Unbiased Look at Dataset Bias. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011

2011

Earlier work this paper cites.

Gong, Boqing, Yuan Shi, Fei Sha, and Kristen Grauman. ”Geodesic flow kernel for unsupervised domain adaptation.” In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 2066-2073. IEEE, 2012. 0

2012

Earlier work this paper cites.

Fernando, Basura, Amaury Habrard, Marc Sebban, and Tinne Tuytelaars. ”Unsupervised visual domain adaptation using subspace alignment.” In Computer Vision (ICCV), 2013 IEEE International Conference on, pp. 2960-2967. IEEE, 2013

2013

Earlier work this paper cites.

Mirrashed, F., Morariu, V. I., Siddiquie, B., Feris, R. S., & Davis, L. S. (2013, January). Domain adaptive object detection. In Applications of Computer Vision (WACV), 2013 IEEE Workshop on (pp. 323-330). IEEE

2013

Cited alongside, same era.

Nguyen, H. V., Ho, H. T., Patel, V. M., & Chellappa, R. (2013). Joint hierarchical domain adaptation and feature learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, submitted

2013

Cited alongside, same era.

Uijlings, J. R., van de Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. International journal of computer vision, 104(2), 154-171

2013

Cited alongside, same era.

Hoffman, Judy, Sergio Guadarrama, Eric S. Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, and Kate Saenko. ”LSDA: Large scale detection through adaptation.” In Advances in Neural Information Processing Systems, pp. 3536-3544. 2014

2014

Cited alongside, same era.

Hoffman, J., Guadarrama, S., Tzeng, E., Hu, R., Donahue, J., Girshick, R., Darrell, T., and Saenko, K. ”LSDA: Large scale detection through adaptation.” In Advances in Neural Information Processing Systems, pp. 3536-3544. 2014

2014

Later among the works it cites.

Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., … & Zitnick, C. L. (2014). Microsoft COCO: Common objects in context. In Computer Vision–ECCV 2014 (pp. 740-755). Springer International Publishing

2014

Later among the works it cites.

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., … & Darrell, T. (2014, November). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the ACM International Conference on Multimedia (pp. 675-678). ACM

2014

Later among the works it cites.

R. Gopalan, R. Li, V.M. Patel and R. Chellappa. Domain Adaptation for Visual Recognition. Foundations and Trends in Computer Graphics and Vision, Vol. 8, No. 4, (2015), pp 285-378

2015