E3S Web Conf.
Volume 245, 20212021 5th International Conference on Advances in Energy, Environment and Chemical Science (AEECS 2021)
|Number of page(s)||5|
|Section||Chemical Performance Research and Chemical Industry Technology Research and Development|
|Published online||24 March 2021|
- S. Bell, C. L. Zitnick, K. Bala, and R. Girshick. Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. arXiv preprint arXiv:1512.04143, 2015. 1, 6 [Google Scholar]
- R. Benenson, M. Omran, J. Hosang, and B. Schiele. Ten years of pedestrian detection, what have we learned? In ECCV, pages 613–627, 2014. 6 [Google Scholar]
- K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv:1405.3531, 2014. 5 [Google Scholar]
- X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, and R. Urtasun. 3d object proposals for accurate object class detection. In NIPS, pages 424–432, 2015. 1 [Google Scholar]
- N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, pages 886–893, 2005. 6 [Google Scholar]
- E. L. Denton, S. Chintala, R. Fergus, et al. Deep generative image models using a laplacian pyramid of adversarial networks. In NIPS, pages 1486–1494, 2015. 2 [Google Scholar]
- P. Doll´ar, R. Appel, S. Belongie, and P. Perona. Fast feature pyramids for object detection. TPAMI, 36(8):1532–1545, 2014. 2, 5 [Google Scholar]
- P. Doll´ar, Z. Tu, P. Perona, and S. Belongie. Integral channel features. In BMVC, volume 2, page 5, 2009. 2 [Google Scholar]
- P. Dollar, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: An evaluation of the state of the art. TPAMI, 34(4):743–761, 2012.1, 2, 5, 6 [Google Scholar]
- M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. 88(2):303–338, 2010. 8 [Google Scholar]
- R. Girshick. Fast r-cnn. In ICCV, pages 1440–1448, 2015. 1, 4, 5, 6, 7, 8 [Google Scholar]
- R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, pages 580–587, 2014. 5 [Google Scholar]
- X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In Aistats, volume 9, pages 249–256, 2010. 5 [Google Scholar]
- I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, pages 2672–2680, 2014. 2, 3 [Google Scholar]
- M. Haloi. A novel plsa based traffic signs classification system. arXiv preprint arXiv:1503.06643, 2015. 2 [Google Scholar]
- K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015. 5 [Google Scholar]
- Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. In ACM Multimedia, pages 675–678, 2014.5 [Google Scholar]
- H. Jiang and S. Wang. Object detection and counting with low quality videos. In Technical Report, 2016. 1 [Google Scholar]
- J. Jin, K. Fu, and C. Zhang. Traffic sign recognition with hinge loss trained convolutional neural networks. IEEE Transactions on Intelligent Transportation Systems, 15(5):1991–2000, 2014. 2 [Google Scholar]
- T. T. Le, S. T. Tran, S. Mita, and T. D. Nguyen. Real time traffic sign detection using color and shape-based features. In Asian Conference on Intelligent Information and Database Systems, pages 268–278. Springer, 2010. 2 [Google Scholar]
- C. Ledig, L. Theis, F. Husz´ar, J. Caballero, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi. Photo-realistic single image superresolution using a generative adversarial network. arXiv preprint arXiv:1609.04802, 2016. 2 [Google Scholar]
- C. Li and M. Wand. Combining markov random fields and convolutional neural networks for image synthesis. arXiv preprint arXiv:1601.04589, 2016. 2 [Google Scholar]
- H. Li, Z. Lin, X. Shen, J. Brandt, and G. Hua. A convolutional neural network cascade for face detection. In CVPR, pages 5325–5334, 2015. 1 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.