[1] CHATFIELD K, SIMONYAN K, VEDALDI A, et al. Return of the devil in the details:Delving deep into convolutional nets[EB/OL].[2016-01-20]. http://arxiv.org/pdf/1405.3531v4.pdf. [2] LIN M, CHEN Q, YAN S. Network in network[EB/OL].[2016-01-20]. http://arxiv.org/pdf/1312.4400v3.pdf. [3] RAZAVIAN A S, AZIZPOUR H, SULLIVAN J, et al. CNN features off-the-shelf:an astounding baseline for recognition[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Washington, DC:IEEE Computer Society, 2014:512-519. [4] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2014:580-587. [5] HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[C]//Proceedings of the 13th European Conference on Computer Vision. Berlin:Springer, 2014:346-361. [6] GIRSHICK R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2015:1440-1448. [7] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, PP(99):1-9. [8] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once:unified, real-time object detection[EB/OL].[2016-01-20]. http://ai2-website.s3.amazonaws.com/publications/YOLO.pdf. [9] LAI K, BO L, REN X, et al. A large-scale hierarchical multi-view RGB-D object dataset[C]//Proceedings of the 28th IEEE International Conference on Robotics and Automation. Piscataway, NJ:IEEE, 2011:1817-1824. [10] BO L, REN X, FOX D. Depth kernel descriptors for object recognition[C]//Proceedings of the 23th IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ:IEEE, 2011:821-826. [11] BO L, REN X, FOX D. Unsupervised feature learning for RGB-D based object recognition[C]//Proceedings of the 13th International Symposium on Experimental Robotics. Berlin:Springer, 2013:387-402. [12] SOCHER R, HUVAL B, BATH B, et al. Convolutional-recursive deep learning for 3D object classification[EB/OL].[2016-01-20]. http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2012_0304.pdf. [13] BAI J, WU Y. SAE-RNN deep learning for RGB-D based object recognition[C]//Proceedings of the 10th International Conference on Intelligent Computing Theory. Berlin:Springer, 2014:235-240. [14] LIANG M, HU X. Recurrent convolutional neural network for object recognition[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2015:3367-3375. [15] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J/OL]. ArXiv.[2016-01-20]. http://www.robots.ox.ac.uk:5000/~vgg/publications/2015/Simonyan15/simonyan15.pdf [16] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems 25. Piscataway, NJ:IEEE:1106-1114. [17] HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J]. Computer Science, 2012, 3(4):212-223. [18] JIA Y, SHELHAMER E, DONAHUE J, et al. Caffe:convolutional architecture for fast feature embedding[C]//Proceedings of the 22th ACM International Conference on Multimedia. New York:ACM, 2014:675-678. [19] GUPTA S, GIRSHICK R, ARBELÁEZ P, et al. Learning rich features from RGB-D images for object detection and segmentation[C]//Proceedings of the 15th European Conference on Computer Vision. Berlin:Springer, 2014:345-360. [20] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE International Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2014:580-587. [21] SILBERMAN, N, HOIEM, D, KOHLI, P, et al. Indoor segmentation and support inference from RGB-D images[C]//Proceedings of the 13th European Conference on Computer Vision. Berlin:Springer, 2012:746-760 [22] REN X, BO L, FOX D. RGB-(D) scene labeling:features and algorithms[C]//Proceedings of the 2012 IEEE International Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2012:2759-2766. [23] ARBELÁEZ P, MAIRE M, FOWLKES C, et al. Contour detection and hierarchical image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(5):898-916. [24] GUPTA S, ARBELAEZ P, MALIK J. Perceptual organization and recognition of indoor scenes from RGB-D images[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2013:564-571. [25] BLUM M, SPRINGENBERG J T, WVLFING J, et al. A learned feature descriptor for object recognition in RGB-D data[C]//Proceedings of the 2012 IEEE International Conference on Robotics and Automation. Piscataway, NJ:IEEE, 2012:1298-1303. |