[1] F.J.Chang, Y.Y.Lin, and K.-J. Hsu, “Multiple structured-instance learning for semantic segmentation with uncertain training data”, Proceedings of the IEEE Computer Vision and Pattern Recognition, pp. 360-367, 2014.
[2] X. Zhu, Y, Xiong, J, Dai, L, Yuan, and Y. Wei,“Deep feature flow for video recognition”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2349–2358, 2017.
[3] D. Lin Y. Li J. Shi, “Low-Latency Video Semantic Segmentation”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[4] P.Hu, F.Caba, O.Wang, Z.Lin, S.Sclaroff and F.Perazzi, “Temporally distributed networks for fast video semantic segmentation”, CVPR, pp. 8818–8827, 2020.
[6] H.Wang, W.Wang and J.Liu, “TEMPORAL MEMORY ATTENTION FOR VIDEO SEMANTIC SEGMENTATION”, CVPR, 2021.
[10] M.Khalooei, M.Fakhredanesh, M.Sabokrou, “Dominant and rare events detection and localization in video using Generative Adversarial Network”,Journal of Soft Computing and Information Technology (JSCIT), Volume 8, Number 3, pp. 40-51, 2019.
[11] M.Fakhredanesh, S.Roostaei, “Action Change Detection in Video Based on HOG”, Journal of Electrical and Computer Engineering Innovations (JECEI), pp. 135-144, 2020.
[12] M. Fayyaz, M. H. Saffar, M. Sabokrou, M. Fathy and R. Klette, “STFCN: spatio-temporal FCN for semantic video segmentation”, CoRR,2016.
[13] P. Fischer, A. Dosovitskiy, E. Ilg, P. Hausser, C. Hazırbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox,“Flownet: Learning optical flow with convolutional networks”, IEEE International Conference on Computer Vision (ICCV), 2015.
[14] E. L. Denton, S. Chintala, R. Fergus, et al., “Deep generative image models using a laplacian pyramid of adversarial networks”, in Proc. Neural Information Processing Systems(NIPS), pp 1486-1494, 2017.
[15] F.Galasso, M.Keuper, T.Brox and B. Schiele, "Spectral graph reduction for efficient image and streaming video segmentation", IEEE Conference on Computer Vision and Pattern Recognition, pp. 49-56, 2014.
[16] A.Khoreva, F.Galasso, M.Hein and B.Schiele, "Classifier based graph construction for video segmentation", Computer Vision and Pattern Recognition (CVPR) 2015 IEEE Conference, pp. 951-960, 2015.
[17] S. Hickson, S. Birchfield, I. Essa, and H. Christensen, "Efficient hierarchical graph-based segmentation of RGBD videos", IEEE Conference on Computer Vision and Pattern Recognition, pp. 344-351, 2014.
[18] S.Ardeshir, K.Malcolm and M.Shah, "Geo-semantic segmentation", IEEE Conference on Computer Vision and Pattern Recognition, pp. 2792-2799, 2015.
[19] G.Bertasius, L.Torresani, S.X.Yu and J.Shi, "Convolutional Random Walk Networks for Semantic Image Segmentation" , arXiv:1605.07681, 2016.
[20] M.P.Kumar, H.Turki, D.Preston and D.Koller, "Parameter estimation and energy minimization for region-based semantic segmentation", IEEE transactions on pattern analysis and machine intelligence, vol. 37, pp. 1373-1386, 2015.
[21] M.Volpi and V.Ferrari, "Semantic segmentation of urban scenes by learning local class interactions", IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1-9, 2015.
[22] A.Sharma, O.Tuzel and D.W.Jacobs, "Deep hierarchical parsing for semantic segmentation", IEEE Conference on Computer Vision and Pattern Recognition, pp. 530- 538, 2015.
[23] Z.Liu, X. Li, P. Luo, C.-C. Loy and X. Tang, "Semantic image segmentation via deep parsing network", IEEE International Conference on Computer Vision, pp. 1377- 1385, 2015.
[24] B. Liu, X. He, and S. Gould, "Multi-class semantic video segmentation with exemplar-based object reasoning", IEEE Winter Conference on Applications of Computer Vision, pp. 1014- 1021, 2015.
[25] L. Sevilla-Lara, D. Sun, V. Jampani, and M. J. Black, "Optical flow with semantic segmentation and localized layers", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
[26] G. Csurka and F. Perronnin, "An efficient approach to semantic segmentation", International Journal of Computer Vision, vol. 95, pp. 198-212, 2011.
[27] C.-F. Tsai, K. McGarry, and J. Tait, "Image classification using hybrid neural networks", 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pp. 431-432, 2003.
[28] T. Blaschke, C. Burnett, and A. Pekkarinen, "Image segmentation methods for object-based analysis and classification", Remote sensing image analysis: Including the spatial domain, ed: Springer, pp. 211-236, 2004.
[29] S.Hochreiter and J.Schmidhuber, “Long short-term memory”, Neural computation, pp. 1735–1780, 1997.
[30] K.Cho, B.Merrienboer, C.Gulc¸ F.Bougares, H.Schwenk and Y.Bengio, “Learning phrase representations using RNN encoder-decoder for statistical machine translation”, EMNLP, 2014.
[31] J.Long, E.Shelhamer, and T.Darrell, “Fully convolutional networks for semantic segmentation”, CVPR, pp. 3431– 3440, 2015.
[32] S.Zheng , “Conditional random fields as recurrent neural networks”, IEEE Int. Conf. Computer Vision, pp. 1529-1537, 2015.
[33] V.Badrinarayanan, A.Kendall and R.Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation”, CoRR, 2015.
[34] H. Zhao, J. Shi, X. Qi, X. Wang and J. Jia “Pyramid scene parsing network”, CVPR, 2017.
[35] A.Kundu, V.Vineet and V.Koltun, “Feature space optimization for semantic video segmentation”, CVPR, 2016.
[37] X.Jin, X.Li, H.Xiao, X.Shen, Z.Lin, J.Yang, Y.Chen, J.Dong, L.Liu and Z.Jie, “Video scene parsing with predictive feature learning”, ICCV, 2017.
[38] S.Jain, X.Wang and J.Gonzalez, “Accel: A corrective fusion network for efficient semantic segmentation on video”, CVPR, 2019.
[39] E. Shelhamer, K. Rakelly, J. Hoffman, and T,“Darrell. Clockwork convnets for video semantic segmentation”, European Conference on Computer Vision (ECCV) Workshops, pp. 852-868 , 2016.
[40] J.Carreira, V.Patraucean, L.Mazare, A.Zisserman and S.Osindero, “Massively parallel video networks”, ECCV, 2018.
[41] Y.He, W.Chiu, M.Keuper and Mario Fritz, “Std2p: Rgbd semantic segmentation using spatio-temporal data-driven pooling”, CVPR, 2017.
[42] G.Hinton, O.Vinyals and J.Dean, “Distilling the knowledge in a neural network”, arXiv:1503.02531, 2015.
[43] G.Huang, Z.Liu, L.V.Maaten and K.Weinberger, “Densely connected convolutional networks”, CVPR, 2017.
[44] S.Chandra, C.Couprie and I.Kokkinos, “Deep Spatio-Temporal Random Fields for Efficient Video Segmentation”, IEEE Conference of Computer Vision and Pattern Recognition, pp. 8915–8924, 2018.
[45] A.Handa, V.Patraucean and R.Cipolla, “Spatio-temporal video autoencoder with differentiable memory”, ICLR Workshop, 2016.
[46] N. Ballas, L. Yao, C. Pal, and A.Courville, “Delving deeper into convolutional networks for learning video representations”, 2016.
[47] R. Gadde, V. Jampani, and P. V. Gehler,“Semantic video cnns through representation warping”,IEEE International Conference on Computer Vision (ICCV), 2017.
[51] Yu and F.Koltun, “Multi-scale context aggregation by dilated convolutions”, ICLR, 2016.
[52]
T.W.Hui,
X.Tang and
C.Ch.Loy, “LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation”, IEEE Conference on Computer Vision and Pattern Recognition (
CVPR), 2018.
[53] X.Li, A.You, Z.Zhu, H.Zhao, M.Yang, K.Yang, Sh.Tan andY.Tong, ‘Semantic Flow for Fast and Accurate Scene Parsing”,
ECCV 2020, pp. 775-793, 2020.
Semantic Segmentation”, CVPR, 2021.
[56] Ch.Yu, J.Wang, Ch.Peng and Ch.Gao, “BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation”, ECCV 2018, pp. 334-349, 2018.
[57] M.D.Yang, J.Boubin, H.P.Tsai and H.Tseng, “Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet”, Computers and Electronics in Agriculture, 2020.
[59]
Y.Liu,
Ch.Shen,
Ch.Yu and
J.Wang, “Efficient Semantic Video Segmentation with Per-Frame Inference”, ECCV, pp.352-368, 2020.
[60]
Y.Hong,
H.Pan,
W.Sun and
Y.Jia, “Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes”, CVPR, 2021.
[61] Ch.Yu, Ch.Gao, J.Wang, G.Yu, Ch.Shen and N.Sang, “BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation”, International Journal of Computer Vision volume 129, p. 3051–3068, 2021.
[62] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs”, ICLR, 2015.