Fetching the paper…
Reading the bibliography…
Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition.
Richer convolutional features for edge detection
Liu, Y., Cheng, M.-M., Hu, X., Bian, J.-W., Zhang, L., Bai, X., et al. (2019) · 1946
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014) · 1958
Earlier work this paper cites.
Camera models and machine perception
Sobel, I. (1970) · 1970
Earlier work this paper cites.
A computational approach to edge detection
Canny, J. (1986) · 1986
Earlier work this paper cites.
Color edge detection using vector order statistics
Trahanias, P. E., & Venetsanopoulos, A. N. (1993) · 1993
Earlier work this paper cites.
Gradient-based edge detection using nonlinear edge enhancing prefilters
Hardie, R. C., & Boncelet, C. G. (1995) · 1995
Earlier work this paper cites.
Automatic gradient threshold determination for edge detection
Henstock, P. V., & Chelberg, D. M. (1996) · 1996
Earlier work this paper cites.
Statistical edge detection: Learning and evaluating edge cues
Konishi, S., Yuille, A. L., Coughlan, J. M., & Zhu, S. C. (2003) · 2003
Earlier work this paper cites.
Learning to detect natural image boundaries using local brightness, color, and texture cues
Martin, D. R., Fowlkes, C. C., & Malik, J. (2004) · 2004
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · 2006
Earlier work this paper cites.
Groups of adjacent contour segments for object detection
Ferrari, V., Fevrier, L., Jurie, F., & Schmid, C. (2008) · 2008
Earlier work this paper cites.
From images to shape models for object detection
Ferrari, V., Jurie, F., & Schmid, C. (2010) · 2010
Earlier work this paper cites.
Rectified linear units improve restricted Boltzmann machines
Nair, V., & Hinton, G. E. (2010) · 2010
Earlier work this paper cites.
Skyline2gps: Localization in urban canyons using omni-skylines
Ramalingam, S., Bouaziz, S., Sturm, P., & Brand, M. (2010) · 2010
Earlier work this paper cites.
Contour detection and hierarchical image segmentation
Arbeláez, P., Maire, M., Fowlkes, C., & Malik, J. (2011) · 2011
Earlier work this paper cites.
Semantic contours from inverse detectors
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., & Malik, J. (2011) · 2011
Earlier work this paper cites.
Sketch tokens: A learned mid-level representation for contour and object detection
Lim, J. J., Zitnick, C. L., & Dollár, P. (2013) · 2013
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., et al. (2014) · 2014
Earlier work this paper cites.
Microsoft COCO: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., et al. (2014) · 2014
Earlier work this paper cites.
Occluding contours for multi-view stereo
Shan, Q., Curless, B., Furukawa, Y., Hernandez, C., & Seitz, S. M. (2014) · 2014
Earlier work this paper cites.
Monocular extraction of 2.1 d sketch using constrained convex optimization
Amer, M. R., Yousefi, S., Raich, R., & Todorovic, S. (2015) · 2015
Earlier work this paper cites.
PCANet: A simple deep learning baseline for image classification?
Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y. (2015) · 2015
Cited alongside, same era.
Fast edge detection using structured forests
Dollár, P., & Zitnick, C. L. (2015) · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., & Szegedy, C. (2015) · 2015
Cited alongside, same era.
Deeply-supervised nets
Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., & Tu, Z. (2015) · 2015
Cited alongside, same era.
DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection
Shen, W., Wang, X., Wang, Y., Bai, X., & Zhang, Z. (2015) · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. (2015) · 2015
Convolutional oriented boundaries: From image segmentation to high-level tasks
Maninis, K.-K., Pont-Tuset, J., Arbelaez, P., & Van Gool, L. (2017) · 2017
Later among the works it cites.
Anti-impulse-noise edge detection via anisotropic morphological directional derivatives
Shui, P.-L., & Wang, F.-P. (2017) · 2017
Later among the works it cites.
Learning multi-instance deep discriminative patterns for image classification
Tang, P., Wang, X., Feng, B., & Liu, W. (2017) · 2017
Later among the works it cites.
Holistically-nested edge detection
Xie, S., & Tu, Z. (2017) · 2017
Later among the works it cites.
Deep edge guided recurrent residual learning for image super-resolution
Yang, W., Feng, J., Yang, J., Zhao, F., Liu, J., Guo, Z., et al. (2017) · 2017
Later among the works it cites.
CASENet: Deep category-aware semantic edge detection
Yu, Z., Feng, C., Liu, M.-Y., & Ramalingam, S. (2017) · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Visual tracking with fully convolutional networks
Wang, L., Ouyang, W., Wang, X., & Lu, H. (2015) · 2015
Cited alongside, same era.
Holistically-nested edge detection
Xie, S., & Tu, Z. (2015) · 2015
Cited alongside, same era.
Semantic segmentation with boundary neural fields
Bertasius, G., Shi, J., & Torresani, L. (2016) · 2016
Cited alongside, same era.
Semantic image segmentation with task-specific edge detection using CNNs and a discriminatively trained domain transform
Chen, L.-C., Barron, J. T., Papandreou, G., Murphy, K., & Yuille, A. L. (2016) · 2016
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., et al. (2016) · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016
Cited alongside, same era.
Later among the works it cites.
Learning to predict crisp boundaries
Deng, R., Shen, C., Liu, S., Wang, H., & Liu, X. (2018) · 2018
Closest in time.
Hou, Q., Liu, J., Cheng, M.-M., Borji, A., & Torr, P. H. (2018) · 2018
Closest in time.
Learning hybrid convolutional features for edge detection
Hu, X., Liu, Y., Wang, K., & Ren, B. (2018) · 2018
Closest in time.
DEL: deep embedding learning for efficient image segmentation
Liu, Y., Jiang, P.-T., Petrosyan, V., Li, S.-J., Bian, J., Zhang, L., et al. (2018) · 2018
Closest in time.
A robust edge detection approach in the presence of high impulse noise intensity through switching adaptive median and fixed weighted mean filtering
Mafi, M., Rajaei, H., Cabrerizo, M., & Adjouadi, M. (2018) · 2018
Closest in time.
Simultaneous edge alignment and learning
Yu, Z., Liu, W., Zou, Y., Feng, C., Ramalingam, S., Kumar, B., et al. (2018) · 2018
Closest in time.
Taskonomy: Disentangling task transfer learning
Zamir, A. R., Sax, A., Shen, W., Guibas, L., Malik, J., & Savarese, S. (2018) · 2018
Closest in time.
Devil is in the edges: Learning semantic boundaries from noisy annotations
Acuna, D., Kar, A., & Fidler, S. (2019) · 2019
Closest in time.
Deeply supervised salient object detection with short connections
Hou, Q., Cheng, M.-M., Hu, X., Borji, A., Tu, Z., & Torr, P. (2019) · 2019
Closest in time.
Dynamic feature fusion for semantic edge detection
Hu, Y., Chen, Y., Li, X., & Feng, J. (2019) · 2019
Closest in time.
Gated-SCNN: Gated shape CNNs for semantic segmentation
Takikawa, T., Acuna, D., Jampani, V., & Fidler, S. (2019) · 2019
Closest in time.
Deep crisp boundaries: From boundaries to higher-level tasks
Wang, Y., Zhao, X., Li, Y., & Huang, K. (2019) · 2019
Closest in time.
Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2020) · 2020
Closest in time.
Unsupervised scale-consistent depth learning from video
Bian, J.-W., Zhan, H., Wang, N., Li, Z., Zhang, L., Shen, C., et al. (2021) · 2021
Closest in time.