Discriminative Models for Multi-Class Object Layout

Histograms of Oriented Gradients for Human Detection

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The pascal visual object classes (VOC) challenge

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Robust Real-Time Face Detection

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2009 IEEE Conference on Computer Vision and Pattern Recognition

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The Hidden Dimension

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An analysis of approximations for maximizing submodular set functions—I

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2013 IEEE International Conference on Computer Vision

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Neural network-based face detection

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A discriminatively trained, multiscale, deformable part model

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The PASCAL visual object classes challenge 2006 (VOC2006) results

Everingham, M., Zisserman, A., Williams, C. K. I., & Gool, L. V. (2006) · Cited by 2,079 · see citing works

Support vector machine learning for interdependent and structured output spaces

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TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation

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Cutting-plane training of structural SVMs

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Convergent Tree-Reweighted Message Passing for Energy Minimization

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Multiscale conditional random fields for image labeling

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Combined Object Categorization and Segmentation With An Implicit Shape Model

Leibe, B., Leonardis, A., & Schiele, B. (2004) · Cited by 811 · see citing works

Putting Objects in Perspective

Hoiem, D., Efros, A. A., & Hebert, M. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0137-5 · Cited by 803 · see citing works

MAP Estimation Via Agreement on Trees: Message-Passing and Linear Programming

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An empirical study of context in object detection

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Object categorization using co-occurrence, location and appearance

Galleguillos, C., Rabinovich, A., & Belongie, S. (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.2008.4587799 · Cited by 467 · see citing works

Optimizing Binary MRFs via Extended Roof Duality

Rother, C., Kolmogorov, V., Lempitsky, V., & Szummer, M. (2007) · 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.2007.383203 · Cited by 424 · see citing works

Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data

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Learning to Localize Objects with Structured Output Regression

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Exploiting hierarchical context on a large database of object categories

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Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes

Murphy, K. P., Torralba, A. B., & Freeman, W. T. (2003) · Cited by 313 · see citing works

Contextual models for object detection using boosted random fields

Torralba, A. B., Murphy, K. P., & Freeman, W. T. (2004) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 303 · see citing works

Learning hierarchical models of scenes, objects, and parts

Sudderth, E. B., Torralba, A. B., Freeman, W. T., & Willsky, A. S. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.137 · Cited by 298 · see citing works

Discriminative models for multi-class object layout

Desai, C., Ramanan, D., & Fowlkes, C. C. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459256 · Cited by 281 · see citing works

Training structural SVMs when exact inference is intractable

Finley, T., & Joachims, T. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390195 · Cited by 260 · see citing works

Near-regular texture analysis and manipulation

Liu, Y., Lin, W., & Hays, J. H. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015731 · Cited by 260 · 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

Multiresolution Models for Object Detection

Park, D., Ramanan, D., & Fowlkes, C. C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_18 · Cited by 232 · see citing works

A hierarchical field framework for unified context-based classification

Kumar, S., & Hebert, M. (2005) · doi:10.1109/iccv.2005.9 · Cited by 226 · see citing works

A scalable modular convex solver for regularized risk minimization

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Linear Programming Relaxations and Belief Propagation -- An Empirical Study

Yanover, C., Meltzer, T., & Weiss, Y. (2006) · Cited by 154 · see citing works

Discriminative models for static human-object interactions

Desai, C., Ramanan, D., & Fowlkes, C. C. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543176 · Cited by 112 · see citing works

Object Detection in 20 Years: A Survey

Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3238524 · Cited by 3,689 · see citing works

Soft-NMS — Improving Object Detection with One Line of Code

Bodla, N., Singh, B., Chellappa, R., & Davis, L. S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.593 · Cited by 2,274 · see citing works

Panoptic Segmentation

Kirillov, A., He, K., Girshick, R., Rother, C., & Dollár, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00963 · Cited by 1,659 · see citing works

Scene Graph Generation by Iterative Message Passing

Xu, D., Zhu, Y., Choy, C. B., & Fei-Fei, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.330 · Cited by 1,271 · see citing works

Image retrieval using scene graphs

Johnson, J., Krishna, R., Stark, M., Li, L.-J., Shamma, D. A., Bernstein, M. S., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298990 · Cited by 1,101 · see citing works

Iterative Visual Reasoning Beyond Convolutions

Chen, X., Li, L.-J., Fei-Fei, L., & Gupta, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00756 · Cited by 236 · see citing works

Non-maximum Suppression for Object Detection by Passing Messages Between Windows

Rothe, R., Guillaumin, M., & Van Gool, L. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-16865-4_19 · Cited by 218 · see citing works

End-to-end integration of a Convolutional Network, Deformable Parts Model and non-maximum suppression

Wan, L., Eigen, D., & Fergus, R. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298686 · Cited by 86 · see citing works

Accurate And Fast Fine-Grained Image Classification via Discriminative Learning

Wang, Z., Wang, S., Zhang, P., Li, H., & Liu, B. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00115 · Cited by 3 · see citing works

Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative FeedbackPreprint

Prabhudesai, M., Ke, T.-W., Li, A. C., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.16102

Stable Discriminative Dictionary Learning Via Discriminative Deviation

Khan, N., & Tappen, M. F. (2012) · Journal of International Crisis and Risk Communication Research · Cited by 6 · see citing works

Discriminative Regions: A Substrate for Analyzing Life-Logging Image Sequences

Moghimi, M. K., Kerr, J., Johnson, E., Godbole, S. V., & Belongie, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14442-9_42 · Cited by 2 · see citing works

Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification

Kangbo, S. U. N., & Zhu, J. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7094 · Cited by 6 · see citing works