The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Superpixel Segmentation With Fully Convolutional Networks
Open accessAbstract
In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing. But only a few attempts have been made to incorporate them into deep neural networks. One main reason is that the standard convolution operation is defined on regular grids and becomes inefficient when applied to superpixels. Inspired by an initialization strategy commonly adopted by traditional superpixel algorithms, we present a novel method that employs a simple fully convolutional network to predict superpixels on a regular image grid. Experimental results on benchmark datasets show that our method achieves state-of-the-art superpixel segmentation performance while running at about 50fps. Based on the predicted superpixels, we further develop a downsampling/upsampling scheme for deep networks with the goal of generating high-resolution outputs for dense prediction tasks. Specifically, we modify a popular network architecture for stereo matching to simultaneously predict superpixels and disparities. We show that improved disparity estimation accuracy can be obtained on public datasets.
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Cites
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.120 · Cited by 9,201
Deformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760
Contour Detection and Hierarchical Image Segmentation
Arbeláez, P., Maire, M., Fowlkes, C., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,454
Deformable ConvNets V2: More Deformable, Better Results
Zhu, X., Hu, H., Lin, S., & Dai, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00953 · Cited by 2,943
Saliency Detection via Graph-Based Manifold Ranking
Yang, C., Zhang, L., Lu, H., Ruan, X., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.407 · Cited by 2,554
Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., & Brox, T. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.438 · Cited by 2,488
Pyramid Stereo Matching Network
Chang, J.-R., & Chen, Y.-S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00567 · Cited by 1,807
End-to-End Learning of Geometry and Context for Deep Stereo Regression
Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P., Kennedy, R., Bachrach, A., & Bry, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.17 · Cited by 1,470
High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth
Scharstein, D., Hirschmüller, H., Kitajima, Y., Krathwohl, G., Nešić, N., Wang, X., & Westling, P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-11752-2_3 · Cited by 1,305
Cited by
Smart Watermark to Defend against Deepfake Image Manipulation
Lv, L. (2021) · 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs52626.2021.9449287 · Cited by 24
Metadata
- Authors
- Yang, F., Sun, Q., Jin, H., & Zhou, Z.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 13961-13970
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035241330
- Cited by
- 257
- References
- 47
- FWCI
- 10.46
- Type
- conference paper
- Language
- en
- Open access
- green
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- Yang, F., Sun, Q., Jin, H., & Zhou, Z. (2020). Superpixel Segmentation With Fully Convolutional Networks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.01398
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