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Results 1-10 of 13 (Search time: 0.004 seconds).
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PreviewIssue DateTitleAuthor(s)
2016Learning local image descriptors with deep siamese and triplet convolutional networks by minimizing global loss functionsVijay Kumar, B.; Carneiro, G.; Reid, I.; 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) (26 Jun 2016 - 01 Jul 2016 : Las Vegas, NV)
2016Unsupervised CNN for single view depth estimation: geometry to the rescueGarg, R.; Vijay Kumar, B.; Carneiro, G.; Reid, I.; 14th European Conference on Computer Vision (ECCV 2016) (11 Oct 2016 - 14 Oct 2016 : Amsterdam, Netherlands)
2016Exploring context with deep structured models for semantic segmentationLin, G.; Shen, C.; Hengel, A.; Reid, I.
2016Fast training of triplet-based deep binary embedding networksZhuang, B.; Lin, G.; Shen, C.; Reid, I.; Computer Society Conference on Computer Vision and Pattern Recognition (CVPRW) (27 Jun 2016 - 30 Jun 2016 : Las Vegas, NV)
2016Learning depth from single monocular images using deep convolutional neural fieldsLiu, F.; Shen, C.; Lin, G.; Reid, I.
2016Efficient piecewise training of deep structured models for semantic segmentationLin, G.; Shen, C.; Van Den Hengel, A.; Reid, I.; 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) (26 Jun 2016 - 01 Jul 2016 : Las Vegas, NV)
2016Efficient point process inference for large-scale object detectionPham, T.; Rezatofighi, S.; Reid, I.; Chin, T.; 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) (26 Jun 2016 - 01 Jul 2016 : Las Vegas, NV)
2016Joint probabilistic matching using m-best solutionsRezatofighi, S.; Milan, A.; Zhang, Z.; Shi, Q.; Dick, A.; Reid, I.; 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2016) (26 Jun 2016 - 01 Jul 2016 : Las Vegas, NV)
2016Direct semi-dense SLAM for rolling shutter camerasKim, J.; Cadena, C.; Reid, I.; 2016 IEEE International Conference on Robotics and Automation (ICRA 2016) (16 May 2016 - 21 May 2016 : Stockholm, Sweden)
2016Online unsupervised feature learning for visual trackingLiu, F.; Shen, C.; Reid, I.; Van Den Hengel, A.