Learning to Localize Objects with Structured Output Regression

Histograms of Oriented Gradients for Human Detection

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Rapid object detection using a boosted cascade of simple features

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SURF: Speeded Up Robust Features

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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

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A discriminatively trained, multiscale, deformable part model

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The PASCAL visual object classes challenge 2006 (VOC2006) results

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Support vector machine learning for interdependent and structured output spaces

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Representing shape with a spatial pyramid kernel

Bosch, A., Zisserman, A., & Muñoz, X. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282340 · Cited by 1,423

Sampling Strategies for Bag-of-Features Image Classification

Nowak, E., Jurie, F., & Triggs, B. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744085_38 · Cited by 927

Combined Object Categorization and Segmentation With An Implicit Shape Model

Leibe, B., Leonardis, A., & Schiele, B. (2004) · Cited by 810

Task-dependent multi-task multiple kernel learning for facial action unit detection

Zhang, X., & Mahoor, M. H. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.08.026 · Cited by 37

Monocular reconstruction of vehicles: Combining SLAM with shape priors

Chhaya, F., Reddy, D. S., Upadhyay, S., Chari, V., Zia, M. Z., & Krishna, K. M. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487799 · Cited by 37

Tracking Randomly Moving Objects on Edge Box ProposalsPreprint

Zhu, G., Porikli, F., & Li, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.08085 · Cited by 43

A survey on recent object detection techniques useful for monocular vision-based planetary terrain classification

Gao, Y., Spiteri, C., Pham, M.-T., & Al-Milli, S. (2014) · Robotics Auton. Syst. · doi:10.1016/j.robot.2013.11.003 · Cited by 38

Learning discriminative localization from weakly labeled data

Hoai, M., Torresani, L., la Torre, F. D., & Rother, C. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.09.028 · Cited by 37

Structural SVM for visual localization and continuous state estimation

Ionescu, C., Bo, L., & Sminchisescu, C. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459346 · Cited by 35

Spatially Regularized Structural Support Vector Machine for Robust Visual Tracking

Zheng, Y., Sun, L., Wang, S., Zhang, J., & Ning, J. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2855686 · Cited by 36

IODA: An input/output deep architecture for image labeling

Lerouge, J., Hérault, R., Chatelain, C., Jardin, F., & Modzelewski, R. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.03.017 · Cited by 34

Quantitative Characterization of Semantic Gaps for Learning Complexity Estimation and Inference Model Selection

Jianping Fan, B., He, X., Zhou, N., Peng, J., & Jain, R. (2012) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2012.2197604 · Cited by 31

Modeling Occlusion by Discriminative AND-OR Structures

Li, B. H., Hu, W., Wu, T., & Zhu, S. C. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.318 · Cited by 33

Hierarchical ranking of facial attributes

Datta, A., Feris, R., & Vaquero, D. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771429 · Cited by 32

Maximum Margin Multi-Label Structured Prediction

Lampert, C. H. (2011) · Cited by 32

CasTabDetectoRS: Cascade Network for Table Detection in Document Images with Recursive Feature Pyramid and Switchable Atrous Convolution

Hashmi, K. A., Pagani, A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2021) · Journal of Imaging · doi:10.3390/jimaging7100214 · Cited by 32

Learning Submodular Losses with the Lovász Hinge

Yu, J., & Blaschko, M. B. (2015) · HAL · Cited by 33

Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization

Shapovalova, N., Raptis, M., Sigal, L., & Mori, G. (2013) · Cited by 31

Action detection using multiple spatial-temporal interest point features

Cao, L., Tian, Y., Liu, Z., Yao, B., Zhang, Z., & Huang, T. S. (2010) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2010.5583562 · Cited by 31

Feature-centric Efficient Subwindow Search

Lehmann, A., Leibe, B., & Gool, L. V. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459341 · Cited by 31

The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses

Yu, J., & Blaschko, M. B. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2883039 · Cited by 31

Fast Edge Detection Using Structured Forests

Neelsidhi Atlantis, P., & Nair, B. (2016) · INTERNATIONAL JOURNAL OF EMERGING TRENDS IN SCIENCE AND TECHNOLOGY · doi:10.18535/ijetst/v3i08.06 · Cited by 29

Learning equivariant structured output SVM regressors

Vedaldi, A., Blaschko, M. B., & Zisserman, A. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126339 · Cited by 29

Structured prediction by joint kernel support estimation

Lampert, C. H., & Blaschko, M. B. (2009) · Machine Learning · doi:10.1007/s10994-009-5111-0 · Cited by 29

Field-based multispecies weed and crop detection using ground robots and advanced YOLO models: A data and model-centric approach

Sunil, G. C., Upadhyay, A., Zhang, Y., Howatt, K., Peters, T. J., Ostlie, M. H., Aderholdt, W., & Sun, X. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100538 · Cited by 28

Saliency-Aware Nonparametric Foreground Annotation Based on Weakly Labeled Data

Cao, X., Zhang, C., Fu, H., Guo, X., & Tian, Q. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2488637 · Cited by 28

Structured Max-Margin Learning for Inter-Related Classifier Training and Multilabel Image Annotation

Jianping Fan, B., Shen, Y., Yang, C., & Zhou, N. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2073476 · Cited by 26

Structured Output SVM for Remote Sensing Image Classification

Tuia, D., Muñoz-Marı́, J., Kanevski, M., & Camps‐Valls, G. (2010) · Journal of Signal Processing Systems · doi:10.1007/s11265-010-0483-8 · Cited by 26

Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images

Hashmi, K. A., Pagani, A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2021) · Applied Sciences · doi:10.3390/app11167610 · Cited by 26

Graph-Regularized Structured Support Vector Machine for Object Tracking

Zhang, S., Sui, Y., Zhao, S., & Zhang, L. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2513659 · Cited by 25

Predictive Learning in the Presence of Heterogeneity and Limited

Karpatne, A., Khandelwal, A., Boriah, S., & Kumar, V. (2014) · Proceedings of the 2014 SIAM International Conference on Data Mining · doi:10.1137/1.9781611973440.29 · Cited by 25

Optimizing Complex Loss Functions in Structured Prediction

Ranjbar, M., Mori, G., & Wang, Y. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15552-9_42 · Cited by 24

Power SVM: Generalization with exemplar classification uncertainty

Zhang, W., Yu, S. X., & Teng, S. (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.2012.6247921 · Cited by 24

Multi-Task Structure-Aware Context Modeling for Robust Keypoint-Based Object Tracking

Li, X., Zhao, L.-M., Ji, W., Wu, Y., Wu, F. Q., Yang, M., Tao, D., & Reid, I. D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2818132 · Cited by 24

A survey of deep learning techniques for detecting and recognizing objects in complex environments

Dogra, A. K., Sharma, V., & Sohal, H. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100686 · Cited by 25

Integrated real-time object detection for self-driving vehicles

Naghavi, S. H., Avaznia, C., & Talebi, H. (2017) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/iranianmvip.2017.8342340 · Cited by 19

Global Interactions in Random Field Models: A Potential Function Ensuring Connectedness

Nowozin, S., & Lampert, C. H. (2010) · SIAM Journal on Imaging Sciences · doi:10.1137/090752614 · Cited by 23

An object detector based on multiscale sliding window search using a fully pipelined binarized CNN on an FPGA

Nakahara, H., Yonekawa, H., & Sato, S. (2017) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/fpt.2017.8280135 · Cited by 23

Efficient Maximum Appearance Search for Large-Scale Object Detection

Chen, Q. K., Zheng, S., Feris, R. S., Datta, A., Cao, L., Huang, Z., & Yan, S. (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.2013.410 · Cited by 23

Part-Based Robust Tracking Using Online Latent Structured Learning

Yao, R., Shi, Q., Shen, C., Zhang, Y., & van den Hengel, A. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2527358 · Cited by 19

Learning with Interpretable Structure from RNN.Preprint

Hou, B., & Zhou, Z. (2018) · arXiv (Cornell University) · Cited by 22

Next-Generation Artificial Intelligence Techniques for Satellite Data Processing

Sisodiya, N., Dube, N., & Thakkar, P. (2019) · Remote sensing and digital image processing · doi:10.1007/978-3-030-24178-0_11 · Cited by 21

Branch and Bound Strategies for Non-maximal Suppression in Object Detection

Blaschko, M. B. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23094-3_28 · Cited by 21

Distribution-Aware Margin Calibration for Semantic Segmentation in Images

Yu, L., Li, Z., Xu, M. G., Gao, Y., Luo, J., & Zhang, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01533-0 · Cited by 20

A multi-plane block-coordinate frank-wolfe algorithm for training structural SVMs with a costly max-oracle

Shah, N., Kolmogorov, V., & Lampert, C. H. (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.2015.7298890 · Cited by 20

Optimizing Nondecomposable Loss Functions in Structured Prediction

Ranjbar, M., Lan, T., Wang, Y., Robinovitch, S. N., Li, Z.-N., & Mori, G. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.168 · Cited by 19

Real-time fast moving object tracking in severely degraded videos captured by unmanned aerial vehicle

Liu, S., & Feng, Y. (2018) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881418759108 · Cited by 18

Weighted Part Context Learning for Visual Tracking

Zhu, G., Wang, J., Zhao, C., & Lu, H. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2479460 · Cited by 18

An efficient approach to content-based object retrieval in videos

Hong, C., Li, N., Song, M., Bu, J., & Chen, C. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.06.025 · Cited by 17

Model-Free Tracker for Multiple Objects Using Joint Appearance and Motion Inference

Liu, C., Yao, R., Rezatofighi, S. H., Reid, I. D., & Shi, Q. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2928123 · Cited by 17

End-to-end training of object class detectors for mean average precisionPreprint

Henderson, P. D., & Ferrari, V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.03476 · Cited by 23

Simultaneous body part and motion identification for human-following robots

Ding, S., Zhai, Q., Li, Y., Zhu, J., Zheng, Y. F., & Xuan, D. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.08.020 · Cited by 16

Implicit Shape Kernel for Discriminative Learning of the Hough Transform Detector

Zhang, Y., & Chen, T. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.105 · Cited by 16

Structured partial least squares for simultaneous object tracking and segmentation

Zhong, B., Yuan, X., Ji, R., Yan, Y., Cui, Z., Hong, X., Chen, Y., Wang, T., Chen, D., & Yu, J. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.11.004 · Cited by 16

Shape classification through structured learning of matching measures

Chen, L., McAuley, J., Feris, R., Caetano, T. S., & Turk, M. (2009) · 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.2009.5206792 · Cited by 16

Position Preserving Multi-Output Prediction

Abraham, Z., Tan, P., Perdinan, Winkler, J. A., Zhong, S., & Liszewska, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40991-2_21 · Cited by 16

Online flowchart understanding by combining max-margin Markov random field with grammatical analysis

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Taxonomic Multi-class Prediction and Person Layout Using Efficient Structured Ranking

Mittal, A., Blaschko, M. B., Zisserman, A., & Torr, P. H. S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_18 · Cited by 15

Branch&Rank: Non-Linear Object Detection

Lehmann, A., Gehler, P., & Gool, L. V. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.8 · Cited by 14

2D Object Detection: A Survey

Malagoli, E., & Persio, L. D. (2025) · Mathematics · doi:10.3390/math13060893 · Cited by 19

Spatial adjacent bag of features with multiple superpixels for object segmentation and classification

Tao, W., Zhou, Y., Liu, L., Li, K., Sun, K., & Zhang, Z. (2014) · Information Sciences · doi:10.1016/j.ins.2014.05.032 · Cited by 13

Improving image annotation via ranking‐oriented neighbor search and learning‐based keyword propagation

Cui, C., Ma, J., Lian, T., Chen, Z., & Wang, S. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23163 · Cited by 13

An Attention-based Regression Model for Grounding Textual Phrases in Images

Ko, E., Aono, M., Nichols, E., & Funakoshi, K. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/558 · Cited by 13

Active Structured Learning for High-Speed Object Detection

Lampert, C. H., & Peters, J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03798-6_23 · Cited by 13

Online object tracking via motion-guided convolutional neural network (MGNet)

Gan, W., Lee, M., Wu, C.-H., & Kuo, C. ‐. J. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.03.016 · Cited by 12

Learning a Family of Detectors via Multiplicative Kernels

Yuan, Q., Thangali, A., Ablavsky, V., & Sclaroff, S. (2013) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-94-007-0726-9_1 · Cited by 12

StructBoost: Boosting Methods for Predicting Structured Output Variables

Shen, C., Lin, G., & van den Hengel, A. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2315792 · Cited by 12

Robobarista: Learning to Manipulate Novel Objects via Deep Multimodal EmbeddingPreprint

Sung, J., Jin, S., Lenz, I., & Saxena, A. (2016) · arXiv.org · doi:10.48550/arxiv.1601.02705 · Cited by 16

Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images

Sinha, S., Hashmi, K. A., Pagani, A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2022) · Applied Sciences · doi:10.3390/app122010578 · Cited by 11

Training many-parameter shape-from-shading models using a surface database

Khan, N., Tran, L., & Tappen, M. F. (2009) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2009.5457444 · Cited by 11

Classification of 3D Archaeological Objects Using Multi-View Curvature Structure Signatures

Canul‐Ku, M., Hasimoto-Beltrán, R., Jiménez-Badillo, D., Ruíz-Correa, S., & Román-Rangel, E. (2018) · IEEE Access · doi:10.1109/access.2018.2886791 · Cited by 10

Inductive manifold learning using structured support vector machine

Kim, K., & Lee, D. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.07.011 · Cited by 11

Deep Kernel machines: a survey

Nair, N. K., Afzal, A. L., & Asharaf, S. (2020) · Pattern Analysis and Applications · doi:10.1007/s10044-020-00933-1 · Cited by 10

Human pose estimation using structural support vector machines

Chen, K., Gong, S., & Xiang, T. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130340 · Cited by 10

Learning human pose in crowd

Gong, S., Xiang, T., & Hongeng, S. (2010) · doi:10.1145/1878039.1878050 · Cited by 10

A principled approach to remove false alarms by modelling the context of a face detector

Atanasoaei, C., McCool, C., & Marcel, S. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.17 · Cited by 9

Extreme learning machine for structured output spaces

Maliha, A., Yusof, R. B., & Shapiai, M. I. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2754-1 · Cited by 9

Assignment-Space-based Multi-Object Tracking and Segmentation

Choudhuri, A., Chowdhary, G. V., & Schwing, A. G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01334 · Cited by 9

Structured output SVM for remote sensing image classification

Tuia, D., Kanevski, M., Muñoz-Marı́, J., & Camps‐Valls, G. (2009) · Machine learning for signal processing ... · doi:10.1109/mlsp.2009.5306235 · Cited by 9

A Decisive Object Detection using Deep Learning Techniques

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Not All Negatives Are Equal: Learning to Track With Multiple Background Clusters

Zhu, G., Porikli, F., & Li, H. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2615518 · Cited by 8

Model-Free Multiple Object Tracking with Shared Proposals

Zhu, G., Porikli, F., & Li, H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54184-6_18 · Cited by 8

Fast concurrent object localization and recognition

YEH, T. P., Lee, J.-Y., & Darrell, T. (2009) · 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/cvprw.2009.5206805 · Cited by 8

Robust Model-Free Multi-Object Tracking with Online Kernelized Structural Learning

Yao, R. (2015) · IEEE Signal Processing Letters · doi:10.1109/lsp.2015.2488678 · Cited by 8

Object Classification with Adaptable Regions

Bilen, H., Pedersoli, M., Namboodiri, V. P., Tuytelaars, T., & Gool, L. V. (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.2014.468 · Cited by 8

Learning Local Patch Orientation with a Cascade of Sparse Regressors

Pagani, A., & Stricker, D. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.86 · Cited by 8

Lie-Struck: Affine Tracking on Lie Groups Using Structured SVM

Zhu, G., Porikli, F., Ming, Y., & Li, H. (2015) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2015.16 · Cited by 7

MOSRO: Enabling Mobile Sensing for Real-Scene Objects with Grid Based Structured Output Learning

Chi, H.-Y., Cheng, W.-H., Chen⋆, M.-S., & Tsui, A. W. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-04114-8_18 · Cited by 7

SubmodBoxes: near-optimal search for a set of diverse object proposals

Sun, Q., & Batra, D. (2015) · neural information processing systems · Cited by 7

Entropy-Based Latent Structured Output Prediction

Bouchacourt, D., Nowozin, S., & Kumar, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.334 · Cited by 7

Perceptually Inspired Layout-Aware Losses for Image Segmentation

Osokin, A., & Kohli, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10605-2_43 · Cited by 7

GlandVision: A Novel Polar Space Random Field Model for Glandular Biological Structure Detection

Fu, H., Qiu, G., Ilyas, M., & Shu, J. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.42 · Cited by 7

Feature-Based Pose Estimation

Sminchisescu, C., Bo, L., Ionescu, C., & Kanaujia, A. (2011) · Springer eBooks · doi:10.1007/978-0-85729-997-0_12 · Cited by 7

Transductive Regression for Data With Latent Dependence Structure

Görnitz, N., Lima, L. A., Varella, L. E. S., Müller, K., & Nakajima, S. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2700429 · Cited by 7

Object detection based on scale-invariant partial shape matching

Fan, H., Cong, Y., & Tang, Y. (2015) · Machine Vision and Applications · doi:10.1007/s00138-015-0693-y · Cited by 7

Learning Deep Neural Networks for High Dimensional Output Problems

Labbé, B., Hérault, R., & Chatelain, C. (2009) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2009.48 · Cited by 7

A Convex Surrogate Operator for General Non-Modular Loss FunctionsPreprint

Yu, J., & Blaschko, M. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.03373 · Cited by 7

Recognition of Medicine using CNN for Visually Impaired

Monica., R., Nirmala, K., Divya, B., & Suganthi., L. (2020) · doi:10.1109/iccsp48568.2020.9182085 · Cited by 6

BFF R-CNN: Balanced Feature Fusion for Object Detection

Liu, H., Wang, N., Xuewei, L. I., Xu, C., & Yaze, L. I. (2022) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7261 · Cited by 6

A comparison of local features for camera-based document image retrieval and spotting

Dang, Q. B., Coustaty, M., Luqman, M. M., & Ogier, J.-M. (2019) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-019-00329-w · Cited by 6

Optimized visual recognition algorithm in service robots

Wu, J., Cai, W., Yu, S. M., Xu, Z. L., & He, X. Y. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881420925308 · Cited by 8

Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments

Hashmi, K. A., Pagani, A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2022) · Sensors · doi:10.3390/s22103703 · Cited by 6

Learning weighted part models for object tracking

Zhao, C., Wang, J., Zhu, G., Wu, Y., & Lu, H. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.10.004 · Cited by 6

Beyond sliding windows: Object localization by efficient subwindow search

Lampert, C. H., Blaschko, M. B., & Hofmann, T. (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.2008.4587586 · Cited by 693

Efficient Subwindow Search: A Branch and Bound Framework for Object Localization

Lampert, C. H., Blaschko, M. B., & Hofmann, T. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.144 · Cited by 342

Efficient object detection via structured learning and local classifiers

Zhang, Z. (2013) · Cited by 1

Kernel Methods in Computer Vision: Object Localization, Clustering, and Taxonomy Discovery

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