Hypercolumns for object segmentation and fine-grained localization

Very Deep Convolutional Networks for Large-Scale Image Recognition

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ImageNet classification with deep convolutional neural networks

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Fully convolutional networks for semantic segmentation

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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

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

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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

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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

Backpropagation Applied to Handwritten Zip Code Recognition

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Object Detection with Discriminatively Trained Part-Based Models

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Two-Stream Convolutional Networks for Action Recognition in Videos

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LIBLINEAR: A Library for Large Linear Classification

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The Laplacian pyramid as a compact image code

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DeepPose: Human Pose Estimation via Deep Neural Networks

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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

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High Accuracy Optical Flow Estimation Based on a Theory for Warping

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Learning Hierarchical Features for Scene Labeling

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Joint Training of a Convolutional Network and a Graphical Model for Human Pose EstimationPreprint

Tompson, J., Jain, A., LeCun, Y., & Bregler, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2984 · Cited by 1,317

Part-Based R-CNNs for Fine-Grained Category Detection

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Simultaneous Detection and Segmentation

Hariharan, B., Arbeláez, P., Girshick, R., & Malik, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10584-0_20 · Cited by 1,189

Multiscale Combinatorial Grouping

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Preattentive texture discrimination with early vision mechanisms

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Articulated Human Detection with Flexible Mixtures of Parts

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Pedestrian Detection with Unsupervised Multi-stage Feature Learning

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Representation of local geometry in the visual system

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Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts

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Bird Species Categorization Using Pose Normalized Deep Convolutional NetsPreprint

Branson, S., Horn, G. V., Belongie, S., & Perona, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2952 · Cited by 600

Parsing clothing in fashion photographs

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Semantic Segmentation with Second-Order Pooling

Carreira, J., Caseiro, R., Batista, J. P., & Sminchisescu, C. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33786-4_32 · Cited by 468

Convolutional feature masking for joint object and stuff segmentation

Dai, J., He, K., & Sun, J. (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.2015.7299025 · Cited by 437

Detecting People Using Mutually Consistent Poselet Activations

Bourdev, L. D., Maji, S., Brox, T., & Malik, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3_13 · Cited by 369

Class-Specific, Top-Down Segmentation

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Paper Doll Parsing: Retrieving Similar Styles to Parse Clothing Items

Yamaguchi, K., Kiapour, M. H., & Berg, T. L. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.437 · Cited by 280

Robust computation of optical flow in a multi-scale differential framework

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Using k-Poselets for Detecting People and Localizing Their Keypoints

Gkioxari, G., Hariharan, B., Girshick, R., & Malik, J. (2014) · 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.2014.458 · Cited by 175

R-CNNs for Pose Estimation and Action Detection

Gkioxari, G., Hariharan, B., Girshick, R., & Malik, J. (2014) · CaltechAUTHORS (California Institute of Technology) · doi:10.48550/arxiv.1406.5212 · Cited by 143

Layered Object Models for Image Segmentation

Yang, Y., Hallman, S., Ramanan, D., & Fowlkes, C. C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.208 · Cited by 142

Pedestrian Parsing via Deep Decompositional Network

Luo, P., Wang, X., & Tang, X. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.329 · Cited by 135

Bottom-Up Segmentation for Top-Down Detection

Fidler, S., Mottaghi, R., Yuille, A., & Urtasun, R. (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.2013.423 · Cited by 127

The truth about cats and dogs

Parkhi, O., Vedaldi, A., Jawahar, C. V., & Zisserman, A. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126398 · Cited by 113

Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation

Ionescu, C., Carreira, J., & Sminchisescu, C. (2014) · 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.2014.215 · Cited by 101

Towards Unified Object Detection and Semantic Segmentation

Dong, J., Chen, Q., Yan, S., & Yuille, A. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_20 · Cited by 70

Shape-based pedestrian parsing

Bo, Y., & Fowlkes, C. C. (2011) · doi:10.1109/cvpr.2011.5995609 · Cited by 70

Determining three-dimensional shape from orientation and spatial frequency disparities

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Learning to localize detected objects

Dai, Q., & Hoiem, D. (2012) · 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.2012.6248070 · Cited by 42

Improved Bird Species Recognition Using Pose Normalized Deep Convolutional Nets

Branson, S., Horn, G. V., Perona, P., & Belongie, S. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.87 · Cited by 17

Volumetric Semantic Segmentation Using Pyramid Context Features

Barron, J. T., Biggin, M. D., Arbelaez, P., Knowles, D. W., Keranen, S. V. E., & Malik, J. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.428 · Cited by 15

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566

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

Untitled work

(2016) · Cited by 31,590

Feature Pyramid Networks for Object Detection

Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939

Hypercolumns for object segmentation and fine-grained localization. arXiv:1411.5752

Hariharan, B., Girshick, R., & Malik, J. (2014)

Hypercolumns for Object Segmentation and Finegrained Localization, arXiv:1411.5752v1 [cs.CV

Hariharan, B., Girshick, R., & Malik, J. (2016)

Object Instance Segmentation and Fine-Grained Localization Using Hypercolumns

Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2578328 · Cited by 102

Universidad de los Andes BogotaPreprint

Hariharan, B., Girshick, R., & Malik, J. (2016)

THEME 3Hyperfeatures — Multilevel Local Coding for Visual Recognition

Agarwal, A., Triggs, B., Interaction, T. H.-C., Processing, I., Management, D., & Systems, K. (2013)