Bridging Composite and Real: Towards End-to-End Deep Image Matting (original) (raw)
References
Aksoy, Y., Oh, T. H., Paris, S., Pollefeys, M., & Matusik, W. (2018). Semantic soft segmentation. ACM Transactions on Graphics,37(4), 1–13. Article Google Scholar
Cai, S., Zhang, X., Fan, H., Huang, H., Liu, J., Liu, J., Liu, J., Wang, J., & Sun, J. (2019). Disentangled image matting. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 8819–8828.
Chen, B.C., & Kae, A. (2019). Toward realistic image compositing with adversarial learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8415–8424.
Chen, Q., Li, D., & Tang, C. K. (2013). Knn matting. IEEE Transactions on Pattern Analysis and Machine Intelligence,35(9), 2175–2188. Article Google Scholar
Chen, Q., Ge, T., Xu, Y., Zhang, Z., Yang, X., & Gai, K. (2018). Semantic human matting. In: Proceedings of the ACM International Conference on Multimedia, pp. 618–626.
Chen, Z., Zhang, J., & Tao, D. (2021). Recursive context routing for object detection. International Journal of Computer Vision,129(1), 142–160. Article Google Scholar
Cong, W., Zhang, J., Niu, L., Liu, L., Ling, Z., Li, W., & Zhang, L. (2020). Dovenet: Deep image harmonization via domain verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8394–8403.
Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2009). Bm3d image denoising with shape-adaptive principal component analysis. In: SPARS’09-Signal Processing with Adaptive Sparse Structured Representations
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. Article Google Scholar
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778.
Hou, Q., Liu, & F. (2019). Context-aware image matting for simultaneous foreground and alpha estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4130–4139.
Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132–7141.
Huang, G., Liu, Z., Van, Der Maaten, L., & Weinberger, K.Q. (2017). Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708.
Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105.
Levin, A., Lischinski, D., & Weiss, Y. (2007). A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence,30(2), 228–242. Article Google Scholar
Levin, A., Rav-Acha, A., & Lischinski, D. (2008). Spectral matting. IEEE Transactions on Pattern Analysis and Machine Intelligence,30(10), 1699–1712. Article Google Scholar
Li, X., Liu, K., Dong, Y., & Tao, D. (2017). Patch alignment manifold matting. IEEE Transactions on Neural Networks and Learning Systems,29(7), 3214–3226. ArticleMathSciNet Google Scholar
Li, Y., & Lu, H. (2020). Natural image matting via guided contextual attention. Proceedings of the AAAI Conference on Artificial Intelligence,34, 11450–11457. Article Google Scholar
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C.L. (2014). Microsoft coco: Common objects in context. In: Proceedings of the European Conference on Computer Vision, pp 740–755
Liu, J., Yao, Y., Hou, W., Cui, M., Xie, X., Zhang, C., & Hua, X.s. (2020). Boosting semantic human matting with coarse annotations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8563–8572
Liu, J.J., Hou, Q., Cheng, M.M., Feng, J., & Jiang, J. (2019). A simple pooling-based design for real-time salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Lu, H., Dai, Y., Shen, C., & Xu, S. (2019a). Indices matter: Learning to index for deep image matting. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3266–3275.
Lu, H., Dai, Y., Shen, C., & Xu, S. (2019b). Indices matter: Learning to index for deep image matting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)
Qiao, Y., Liu, Y., Yang, X., Zhou, D., Xu, M., Zhang, Q., & Wei, X. (2020). Attention-guided hierarchical structure aggregation for image matting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., & Jagersand, M. (2019). Basnet: Boundary-aware salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1804–2767. Springer, Germany
Rhemann, C., Rother, C., Wang, J., Gelautz, M., Kohli, P., & Rott, P. (2009). A perceptually motivated online benchmark for image matting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1826–1833.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In: International Conference on MICCAI, pp. 234–241.
Ruzon, M.A., & Tomasi, C. (2000). Alpha estimation in natural images. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 18–25.
Shen, X., Tao, X., Gao, H., Zhou, C., & Jia, J. (2016). Deep automatic portrait matting. In: Proceedings of the European Conference on Computer Vision, pp. 92–107.
Sun, J., Jia, J., Tang, C. K., & Shum, & H.Y. (2004). Poisson matting. ACM Transactions on Graphics,23(3), 315–321.
Tang, J., Aksoy, Y., Oztireli, C., Gross, M., & Aydin, T.O. (2019). Learning-based sampling for natural image matting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3055–3063.
Tsai, Y.H., Shen, X., Lin, Z., Sunkavalli, K., Lu, X., & Yang, M.H. (2017). Deep image harmonization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3789–3797.
Wang, J., & Cohen, M.F. (2005). An iterative optimization approach for unified image segmentation and matting. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 936–943.
Wang, J., & Cohen, M.F. (2007). Optimized color sampling for robust matting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8.
Xu, N., Price, B., Cohen, S., & Huang, T. (2017). Deep image matting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. .2970–2979
Xue, S., Agarwala, A., Dorsey, J., & Rushmeier, H. (2012). Understanding and improving the realism of image composites. ACM Transactions on Graphics,31(4), 1–10.
Yu, Q., Zhang, J., Zhang, H., Wang, Y., Lin, Z., Xu, N., Bai, Y., & Yuille, A. (2021). Mask guided matting via progressive refinement network. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition
Zhang, J., & Tao, D. (2020). Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things. IEEE Internet of Things Journal,8(10), 7789–7817. Article Google Scholar
Zhang, J., Chen, Z., & Tao, D. (2021). Towards high performance human keypoint detection. International Journal of Computer Vision,129(9), 2639–2662. Article Google Scholar
Zhang, Q., Zhang, J., Liu, W., & Tao, D. (2019). Category anchor-guided unsupervised domain adaptation for semantic segmentation. Advances in Neural Information Processing Systems,32, 435–445. Google Scholar
Zhang, Y., Gong, L., Fan, L., Ren, P., Huang, Q., Bao, H., & Xu, W. (2019b). A late fusion cnn for digital matting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7469–7478.
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890.
Zheng, Y., Kambhamettu, C., Yu, J., Bauer, T., & Steiner, K. (2008). Fuzzymatte: A computationally efficient scheme for interactive matting. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8.