Holistically-nested edge detection

Very Deep Convolutional Networks for Large-Scale Image Recognition

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ImageNet Large Scale Visual Recognition Challenge

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Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works

A Computational Approach to Edge Detection

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Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex

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Backpropagation Applied to Handwritten Zip Code Recognition

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The Pascal Visual Object Classes Challenge: A Retrospective

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Theory of edge detection

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Efficient Graph-Based Image Segmentation

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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 · see citing works

Contour Detection and Hierarchical Image Segmentation

Arbeláez, P., Maire, M., Fowlkes, C. C., & Malik, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,472 · see citing works

Holistically-Nested Edge Detection

Xie, S., & Tu, Z. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.164 · Cited by 3,401 · see citing works

Learning Hierarchical Features for Scene Labeling

Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620 · see citing works

Learning to detect natural image boundaries using local brightness, color, and texture cues

Martin, D. R., Fowlkes, C. C., & Malik, J. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1273918 · Cited by 2,424 · see citing works

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

Gupta, S., Girshick, R., Arbeláez, P., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_23 · Cited by 1,528 · see citing works

Hypercolumns for object segmentation and fine-grained localization

Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511 · see citing works

The Role of Context for Object Detection and Semantic Segmentation in the Wild

Mottaghi, R., Chen, X., Liu, X., Cho, N.-G., Lee, S.-W., Fidler, S., Urtasun, R., & Yuille, A. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.119 · Cited by 1,438 · see citing works

Scale-space filtering: A new approach to multi-scale description

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On Edge Detection

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Fast Edge Detection Using Structured Forests

Dollár, P., & Zitnick, C. L. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2377715 · Cited by 965 · see citing works

Statistics of natural images: Scaling in the woods

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Deeplysupervised netsPreprint

Lee, C., Xie, S., Gallagher, P., Zhang, Z., & Tu, Z. (2015) · Cited by 850 · see citing works

Putting Objects in Perspective

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Scaling Theorems for Zero Crossings

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Neural mechanisms of form and motion processing in the primate visual system

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Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images

Gupta, S., Arbeláez, P., & Malik, J. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.79 · Cited by 601 · see citing works

DeepEdge: A multi-scale bifurcated deep network for top-down contour detection

Bertasius, G., Shi, J., & Torresani, L. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299067 · Cited by 549 · see citing works

DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection

Shen, W., Wang, X., Wang, Y., Bai, X., & Zhang, Z. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299024 · Cited by 546 · see citing works

Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection

Lim, J. J., Zitnick, C. L., & Dollár, P. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.406 · Cited by 433 · see citing works

Supervised Learning of Edges and Object Boundaries

Dollár, P., Zhuowen Tu, A., & Belongie, S. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.298 · Cited by 428 · see citing works

Object Contour Detection with a Fully Convolutional Encoder-Decoder Network

Yang, J., Price, B. L., Cohen, S. D., Lee, H., & Yang, M. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.28 · Cited by 395 · see citing works

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. P., & Yuille, A. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.492 · Cited by 377 · see citing works

Nonparametric Scene Parsing via Label Transfer

Liu, C., Yuen, J., & Torralba, A. B. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.131 · Cited by 369 · see citing works

On the accuracy of the Sobel edge detector

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Statistical edge detection: learning and evaluating edge cues

Konishi, S., Yuille, A., Coughlan, J. M., & Zhu, S. C. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1159946 · Cited by 339 · see citing works

Convolutional Neural Networks Applied to House Numbers Digit ClassificationPreprint

Sermanet, P., Chintala, S., & LeCun, Y. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1204.3968 · Cited by 336 · see citing works

Ecological statistics of Gestalt laws for the perceptual organization of contours

Elder, J. H., & Goldberg, R. M. (2002) · Journal of Vision · doi:10.1167/2.4.5 · Cited by 333 · see citing works

$$N^4$$-Fields: Neural Network Nearest Neighbor Fields for Image Transforms

Ganin, Y., & Lempitsky, V. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16808-1_36 · Cited by 262 · see citing works

Discriminatively Trained Sparse Code Gradients for Contour Detection

Ren, X., & Bo, L. (2012) · Cited by 249 · see citing works

Multi-scale Deep Learning for Gesture Detection and Localization

Neverova, N., Wolf, C., Taylor, G. W., & Nebout, F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16178-5_33 · Cited by 239 · see citing works

Auto-context and its application to high-level vision tasks

Zhuowen Tu, A. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587436 · Cited by 239 · see citing works

Recovering Occlusion Boundaries from a Single Image

Hoiem, D., Stein, A. N., Efros, A. A., & Hebert, M. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408985 · Cited by 206 · see citing works

Oriented edge forests for boundary detection

Hallman, S., & Fowlkes, C. C. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298782 · Cited by 193 · see citing works

Multi-scale Improves Boundary Detection in Natural Images

Ren, X. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88690-7_40 · Cited by 190 · see citing works

Pushing the Boundaries of Boundary Detection using Deep LearningPreprint

Kokkinos, I. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.07386 · Cited by 187 · see citing works

Convolutional Oriented Boundaries

Maninis, K.-K., Pont-Tuset, J., Arbeláez, P., & Gool, L. V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_35 · Cited by 140 · see citing works

A systematic comparison between visual cues for boundary detection

Mély, D. A., Kim, J., McGill, M., Guo, Y., & Serre, T. (2016) · Vision Research · doi:10.1016/j.visres.2015.11.007 · Cited by 135 · see citing works

Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs

Shen, W., Zhao, K., Jiang, Y., et al. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.31 · Cited by 97 · see citing works

Dense Volume-to-Volume Vascular Boundary Detection

Merkow, J., Marsden, A. L., Kriegman, D. J., & Zhuowen Tu, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_43 · Cited by 94 · see citing works

Unsupervised Learning of Edges

Li, Y., Paluri, M., Rehg, J. M., & Dollár, P. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.179 · Cited by 94 · see citing works

Pixel-wise Deep Learning for Contour DetectionPreprint

Hwang, J.-J., & Liu, T.-L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.01989 · Cited by 91 · see citing works

Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection

Kivinen, J., Williams, C. K. I., & Heess, N. (2014) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 80 · see citing works

Primacy of spatial information in guiding target selection for pursuit and saccades

Adler, S. A., Bala, J. K., & Krauzlis, R. J. (2002) · Journal of Vision · doi:10.1167/2.9.5 · Cited by 60 · see citing works

Weakly Supervised Object Boundaries

Khoreva, A., Benenson, R., Omran, M., Hein, M., & Schiele, B. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.27 · Cited by 52 · see citing works

Boundary Detection Benchmarking: Beyond F-Measures

Hou, X., Yuille, A., & Christof Koch, A. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.276 · Cited by 46 · see citing works

Multiscale Convolutional Neural Networks for Vision–Based Classification of Cells

Buyssens, P., Elmoataz, A., & Lézoray, O. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37444-9_27 · Cited by 44 · see citing works

Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain TransformPreprint

Chen, L.-C., Barron, J. T., Papandreou, G., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.03328 · Cited by 35 · see citing works

Image-to-Image Translation with Conditional Adversarial Networks

Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · see citing works

Fully convolutional networks for semantic segmentation

Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works

Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

Yang, F., Choi, W., & Lin, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.234 · Cited by 608 · see citing works

Salient Object Detection via Integrity Learning

Zhuge, M., Fan, D.-P., Liu, N., Zhang, D., Xu, D., & Shao, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3179526 · Cited by 403 · see citing works

RGB-T Salient Object Detection via Fusing Multi-Level CNN Features

Zhang, Q., Huang, N., Yao, L., Zhang, D., Shan, C., & Han, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2959253 · Cited by 247 · see citing works

Marr Revisited: 2D-3D Alignment via Surface Normal Prediction

Bansal, A., Russell, B., & Gupta, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.642 · Cited by 213 · see citing works

Segmentation-Aware Convolutional Networks Using Local Attention Masks

Harley, A. W., Derpanis, K. G., & Kokkinos, I. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.539 · Cited by 142 · see citing works

Deep-learning-based Accurate Hepatic Steatosis Quantification for Histological Assessment of Liver Biopsies

Roy, M., Wang, F., Vo, H., Teng, D., Teodoro, G., Farris, A. B., Castillo-Leon, E., Vos, M. B., & Kong, J. (2020) · Laboratory Investigation · doi:10.1038/s41374-020-0463-y · Cited by 68 · see citing works

End-to-end optimization of prosthetic vision

de Ruyter van Steveninck, J., Güçlü, U., van Wezel, R., & van Gerven, M. (2022) · Journal of Vision · doi:10.1167/jov.22.2.20 · Cited by 54 · see citing works

PixArt-$α$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Chen, J., Yu, J., Ge, C., Yao, L., Xie, E., Wu, Y., Wang, Z., Kwok, J., Luo, P., Lu, H., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.00426 · Cited by 30 · see citing works

Weakly- and Semi-Supervised Fast Region-Based CNN for Object Detection

Wang, X.-G., Wang, J.-S., Tang, P., & Liu, W.-Y. (2019) · Journal of Computer Science and Technology · doi:10.1007/s11390-019-1975-z · Cited by 8 · see citing works

End-to-end optimization of prosthetic vision

de Ruyter van Steveninck, J., Güçlü, U., van Wezel, R., & van Gerven, M. (2020) · doi:10.1101/2020.12.19.423601 · Cited by 54 · see citing works

Improved Regional Proposal Generation and Proposal Selection Method for Weakly Supervision Object detection

Wang, Y., & Huo, H. (2023) · Academic Journal of Science and Technology · doi:10.54097/ajst.v5i3.7806

Object detection using improved YOLOv3-tiny based on pyramid pooling

Liang, R., & Yang, T. (2021) · doi:10.1117/12.2599401 · Cited by 1 · see citing works

Improving object detection via improving accuracy of object localization

Zhao, H., Jiang, Z., & Men, A. (2016) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccchina.2016.7636786

Object detection through region proposal based techniques

Prakash, S. R., & Singh, P. N. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.02.533 · Cited by 17 · see citing works

Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going?

Russakovsky, O., Deng, J., Huang, Z., Berg, A. C., & Fei-Fei, L. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.258 · Cited by 84 · see citing works