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

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works

Histograms of Oriented Gradients for Human Detection

Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · 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

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works

Object Detection with Discriminatively Trained Part-Based Models

Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853 · see citing works

The Pascal Visual Object Classes Challenge: A Retrospective

Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139 · see citing works

Selective Search for Object Recognition

Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980 · see citing works

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680 · see citing works

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

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 · see citing works

Deep Neural Networks for Object Detection

Szegedy, C., Toshev, A., & Erhan, D. (2013) · Cited by 1,313 · see citing works

Deformable part models are convolutional neural networks

Girshick, R., Iandola, F., Darrell, T., & Malik, 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.2015.7298641 · Cited by 423 · see citing works

Regionlets for Generic Object Detection

Wang, X., Yang, M., Shenghuo Zhu, B., & Lin, Y. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.10 · Cited by 319 · see citing works

Latent hierarchical structural learning for object detection

Zhu, L., Chen, Y., Yuille, A., & Freeman, W. T. (2010) · 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.2010.5540096 · Cited by 255 · see citing works

Histograms of Sparse Codes for Object Detection

Ren, X., & Ramanan, D. (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.2013.417 · Cited by 239 · see citing works

Discriminative Models for Multi-Class Object Layout

Desai, C., Ramanan, D., & Fowlkes, C. C. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0439-x · Cited by 208 · see citing works

Deformable Part Models are Convolutional Neural NetworksPreprint

Girshick, R., Iandola, F., Darrell, T., & Malik, J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.5403 · Cited by 64 · see citing works

Simultaneous Object Detection and Ranking with Weak Supervision

Blaschko, M. B., Vedaldi, A., & Zisserman, A. (2010) · Cited by 63 · see citing works

Active Mask Hierarchies for Object Detection

Chen, Y., Zhu, L., & Yuille, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15555-0_4 · Cited by 25 · 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

Deep Learning for Generic Object Detection: A Survey

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965 · see citing works

Learning Non-maximum Suppression

Hosang, J., Benenson, R., & Schiele, B. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.685 · Cited by 736 · see citing works

End-to-End People Detection in Crowded Scenes

Stewart, R., Andriluka, M., & Ng, A. Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.255 · Cited by 539 · see citing works

Multi-scale object detection in remote sensing imagery with convolutional neural networks

Deng, Z., Sun, H., Zhou, S., Zhao, J., Lei, L., & Zou, H. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.003 · Cited by 522 · see citing works

Object Detection Networks on Convolutional Feature Maps

Ren, S., He, K., Girshick, R., Zhang, X., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2601099 · Cited by 454 · see citing works

Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition

Ding, C., & Tao, D. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2700390 · Cited by 436 · see citing works

Deformable part models are convolutional neural networks

Girshick, R., Iandola, F., Darrell, T., & Malik, 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.2015.7298641 · Cited by 423 · see citing works

Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection

Cheng, G., Han, J., Zhou, P., & Xu, D. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2867198 · Cited by 408 · see citing works

End-to-End Training of Object Class Detectors for Mean Average Precision

Henderson, P., & Ferrari, V. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54193-8_13 · Cited by 373 · see citing works

WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation

Durand, T., Mordan, T., Thome, N., & Cord, M. (2017) · 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.2017.631 · Cited by 346 · see citing works

Diversity in Machine Learning

Gong, Z., Zhong, P., & Hu, W. (2019) · IEEE Access · doi:10.1109/access.2019.2917620 · Cited by 284 · see citing works

End-to-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation

Yang, W. P., Ouyang, W., Li, H., & Wang, X. (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.2016.335 · Cited by 264 · see citing works

Deeply Learned Compositional Models for Human Pose Estimation

Tang, W., Yu, P., & Wu, Y. N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_12 · Cited by 252 · see citing works

Deep Learning for Generic Object Detection: A Survey

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2018) · arXiv · doi:10.48550/arxiv.1809.02165 · Cited by 2,965 · see citing works

Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review

Aziz, L., Salam, M. S. B. H., Sheikh, U. U., & Ayub, S. (2020) · IEEE Access · doi:10.1109/access.2020.3021508 · Cited by 214 · see citing works

Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection

Papandreou, G., Kokkinos, I., & Savalle, P.-A. (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.2015.7298636 · Cited by 205 · see citing works

RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection

Cheng, G., Zhou, P., & Han, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.315 · Cited by 175 · see citing works

Counting Everyday Objects in Everyday Scenes

Chattopadhyay, P., Vedantam, R., Selvaraju, R. R., Batra, D., & Parikh, D. (2017) · 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.2017.471 · Cited by 160 · see citing works

Recent Advances in Object Detection in the Age of Deep Convolutional Neural NetworksPreprint

Agarwal, S., du Terrail, J. O., & Jurie, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03193 · Cited by 133 · see citing works

Joint Object and Part Segmentation Using Deep Learned Potentials

Wang, P., Shen, X., Lin, Z., Cohen, S. D., Price, B. L., & Yuille, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.184 · Cited by 117 · see citing works

GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection

Kumar, A., Brazil, G., & Liu, X. (2021) · 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/cvpr46437.2021.00886 · Cited by 102 · see citing works

A Convnet for Non-maximum Suppression

Hosang, J., Benenson, R., & Schiele, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45886-1_16 · Cited by 84 · see citing works

Chained Cascade Network for Object Detection

Ouyang, W., Wang, K., Zhu, X., & Wang, X. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.214 · Cited by 83 · see citing works

WP-DRnet: A novel wear particle detection and recognition network for automatic ferrograph image analysis

Peng, Y., Cai, J., Wu, T., Cao, G., Kwok, N. M., & Peng, Z. (2020) · Tribology International · doi:10.1016/j.triboint.2020.106379 · Cited by 79 · see citing works

Multiscale Deep Learning for Detection and Recognition: A Comprehensive Survey

Jiao, L., Wang, M., Liu, X., Li, L., Liu, F., Feng, Z., Yang, S., & Hou, B. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3389454 · Cited by 76 · see citing works

CRF-CNN: Modeling Structured Information in Human Pose EstimationPreprint

Chu, X., Ouyang, W., Li, H., & Wang, X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.00468 · Cited by 61 · see citing works

Adaptive Deep Convolutional Neural Networks for Scene-Specific Object Detection

Li, X., Ye, M., Liu, Y., & Zhu, C. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2749620 · Cited by 55 · see citing works

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection

Shepley, A., Falzon, G., Kwan, P., & Branković, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3273210 · Cited by 52 · see citing works

Fault Detection Method of Glass Insulator Aerial Image Based on the Improved YOLOv5

Ming, Z., Li, B., Wang, J., & He, S. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3269099 · Cited by 51 · see citing works

Accurate object detection using memory-based models in surveillance scenes

Li, X., Ye, M., Liu, Y., Zhang, F., Liu, D., & Tang, S. Y. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.01.030 · Cited by 50 · see citing works

Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection

Wang, K., Lin, L., Zuo, W., Gu, S., & Lei Zhang, 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.2016.235 · Cited by 49 · see citing works

Learning non-maximum suppressionPreprint

Hosang, J., Benenson, R., & Schiele, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.02950 · Cited by 48 · see citing works

End-to-End Learning of Latent Deformable Part-Based Representations for Object Detection

Mordan, T., Thome, N., Hénaff, G., & Cord, M. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1109-z · Cited by 47 · see citing works

Deep Co-occurrence Feature Learning for Visual Object Recognition

Shih, Y.-F., Yeh, Y.-M., Lin, Y., Weng, M.-F., Lu, Y.-C., & Chuang, Y. (2017) · 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.2017.772 · Cited by 45 · see citing works

Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window DetectionPreprint

Papandreou, G., Kokkinos, I., & Savalle, P.-A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.0296 · Cited by 44 · see citing works

Deep Learning for Semantic Part Segmentation with High-Level GuidancePreprint

Tsogkas, S., Kokkinos, I., Papandreou, G., & Vedaldi, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.02438 · Cited by 44 · see citing works

Deformable Template Network (DTN) for Object Detection

Wu, S., Xu, Y. D., Zhang, B., Yang, J. J., & Zhang, D. D. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3075323 · Cited by 39 · see citing works

Multi-scale volumes for deep object detection and localization

Ohn-Bar, E., & Trivedi, M. M. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.06.002 · Cited by 38 · see citing works

Real-Time SSDLite Object Detection on FPGA

Kim, S., Na, S., Kong, B. Y., Choi, J., & Park, I. (2021) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2021.3064639 · Cited by 37 · see citing works

DSN: A New Deformable Subnetwork for Object Detection

Wu, S., & Xu, Y. D. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2905373 · Cited by 33 · see citing works

A Deep CNN-Based Detection Method for Multi-Scale Fine-Grained Objects in Remote Sensing Images

Xie, Q., Zhou, D., Tang, R., & Feng, H. (2024) · IEEE Access · doi:10.1109/access.2024.3356716 · Cited by 32 · see citing works

Multi-Oriented and Scale-Invariant License Plate Detection Based on Convolutional Neural Networks

Han, J. R., Yao, J., Zhao, J., Tu, J., & Liu, Y. (2019) · Sensors · doi:10.3390/s19051175 · Cited by 32 · see citing works

Individualness and Determinantal Point Processes for Pedestrian Detection

Lee, D., Cha, G., Yang, M.-H., & Oh, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_20 · Cited by 31 · see citing works

Syncretic-NMS: A Merging Non-Maximum Suppression Algorithm for Instance Segmentation

Chu, J., Zhang, Y., Li, S., Leng, L., & Miao, J. (2020) · IEEE Access · doi:10.1109/access.2020.3003917 · Cited by 30 · see citing works

Action Temporal Localization in Untrimmed Videos via Multi-stage CNNs.Preprint

Shou, Z., Wang, D., & Chang, S. (2016) · arXiv (Cornell University) · Cited by 26 · see citing works

Multi-Scale Geospatial Object Detection Based on Shallow-Deep Feature Extraction

AL-Alimi, D., Shao, Y., Feng, R., Al‐qaness, M. A. A., Elaziz, M. A., & Kim, S. (2019) · Remote Sensing · doi:10.3390/rs11212525 · Cited by 25 · see citing works

Deformable Part-based Fully Convolutional Network for Object Detection

Mordan, T., Thome, N., Hénaff, G., & Cord, M. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.88 · Cited by 24 · see citing works

Pigeon cleaning behavior detection algorithm based on light-weight network

Guo, J., He, G., Deng, H., Fan, W., Xu, L., Cao, L., Feng, D., Li, J., Wu, H., Lv, J., et al. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107032 · Cited by 23 · see citing works

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 · see citing works

Adaptive Non-Maximum Suppression for improving performance of Rumex detection

Al-Badri, A. H., Ismail, N. A., Al-Dulaimi, K., Salman, G. A., & Salam, M. S. H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119634 · Cited by 22 · see citing works

Learning Detection with Diverse Proposals

Azadi, S., Feng, J., & Darrell, T. (2017) · 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.2017.779 · Cited by 20 · see citing works

Discriminative part model for visual recognition

Sicre, R., & Jurie, F. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.08.002 · Cited by 19 · see citing works

DeePM: A Deep Part-Based Model for Object Detection and Semantic Part LocalizationPreprint

Zhu, J., Chen, X., & Yuille, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.07131 · Cited by 19 · see citing works

Learning to decompose for object detection and instance segmentationPreprint

Park, E., & Berg, A. C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06449 · Cited by 19 · see citing works

Detection algorithm of abnormal flow state fluid on closed vibrating screen based on improved YOLOv5

Wang, G., Chen, S., Hu, G. X., Pang, D., & Wang, Z. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106272 · Cited by 18 · see citing works

Improving Neural Non-Maximum Suppression for Object Detection by Exploiting Interest-Point Detectors

Symeonidis, C., Mademlis, I., Nikolaidis, N., & Pitas, I. (2019) · Machine learning for signal processing ... · doi:10.1109/mlsp.2019.8918769 · Cited by 16 · see citing works

Deformable Part Region Learning and Feature Aggregation Tree Representation for Object Detection

Bae, S. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3268864 · Cited by 14 · see citing works

Face detection with a 3D model

Barbu, A., Lay, N. S., & Gramajo, G. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-811889-4.00006-3 · Cited by 13 · see citing works

Towards a Unified Compositional Model for Visual Pattern Modeling

Tang, W., Yu, P., Zhou, J., & Wu, Y. N. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.303 · Cited by 11 · see citing works

Deep Convolutional Neural Network in Deformable Part Models for Face Detection

Nguyen, D.-L., Nguyen, V.-T., Tran, M., & Yoshitaka, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-29451-3_53 · Cited by 6 · see citing works

Deformable Part Region Learning for Object Detection

Bae, S. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19883 · Cited by 6 · see citing works

Multi-channel and multi-scale mid-level image representation for scene classification

Yang, J., Yang, F., Wang, G., & Li, M. (2017) · Journal of Electronic Imaging · doi:10.1117/1.jei.26.2.023018 · Cited by 6 · see citing works

Improved Efficient Convolutional Neural Networks for Complex Scene Mask-Wearing Detection

Xue, J., Cheng, J., Zhang, Q., Guo, Y., Lu, A., Li, J., Wan, X., & Xu, J. (2021) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2021.18635 · Cited by 4 · see citing works

Learning Hough Transform with Latent Structures for Joint Object Detection and Pose Estimation

Li, H., He, X., Barnes, N. M., & Wang, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27674-8_11 · Cited by 4 · see citing works

Learning to Filter Object Detections

Prokudin, S., Kappler, D., Nowozin, S., & Gehler, P. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66709-6_5 · Cited by 4 · see citing works

Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor

Niu, Y., Qin, L., & Wang, X. (2019) · Sensors · doi:10.3390/s19081766 · Cited by 4 · see citing works

Adaptive WildNet Face network for detecting face in the wild

Nguyen, D.-L., Nguyen, V.-T., Tran, M., & Yoshitaka, A. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2229889 · Cited by 4 · see citing works

Fast deep pyramid DPM object detection with region proposal networks

Ali, A., Olaleye, O. G., Dey, B., & Bayoumi, M. (2017) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2017.8388636 · Cited by 4 · see citing works

High-Speed Motion Target Real-Time Detection Based on Lightweight Deep Feature Learning Network

Bao, Y., Liu, Z., Feng, W., Deng, Y., & Huang, Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3395712 · Cited by 3 · see citing works

Scene-Domain Active Part Models for Object Representation

Ren, Z., Wang, C., & Yuille, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.287 · Cited by 3 · see citing works

Local structured representation for generic object detection

Zhang, J., Huang, K., Tan, T., & Zhang, Z. (2017) · Frontiers of Computer Science · doi:10.1007/s11704-016-5530-6 · Cited by 3 · see citing works

A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering

Zhao, X. W., Wang, Y., & Dou, Y. (2017) · ITM Web of Conferences · doi:10.1051/itmconf/20171205006 · Cited by 2 · see citing works

Machine Learning Based Bounding Box Regression for Improved Pedestrian Detection

Toprak, T., Günel, S., Belenlioglu, B., Aydin, B., Zoral, E. Y., & Selver, M. A. (2019) · International Symposium on Advanced Electrical and Communication Technologies (ISAECT) · doi:10.1109/isaect47714.2019.9069715 · Cited by 2 · see citing works

Object detection based on deep learning

Dong, J. (2021) · International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV) · doi:10.1117/12.2626678 · Cited by 2 · see citing works

Structured svm backpropagation to convolutional neural network applying to human pose estimation

Witoonchart, P. (2016) · doi:10.58837/chula.the.2016.1476 · Cited by 2 · see citing works

A lightweight network for phone surface defect detection with industrial deployment on RK3568 edge devices

Zhuo, S., Zhou, X., Cai, J., Bai, H., Duan, X., & Ma, Y. (2025) · PLoS ONE · doi:10.1371/journal.pone.0336971 · Cited by 1 · see citing works

Improvement of Non-maximum Suppression in Pedestrian Detection Based on HOG Features

Wang, Q., Xu, M., Guo, A., & Ran, F. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-2672-0_31 · Cited by 1 · see citing works

Accelerating Deformable Part Models with Branch-and-Bound

Kokkinos, I. (2016) · Mathematics and visualization · doi:10.1007/978-3-319-24726-7_12 · Cited by 1 · see citing works

Faster R-CNN for Detection of Carotid Plaque on Ultrasound Images

An, X., Ye, G., Zhou, X., Jiao, Z., Ding, S., & Xie, Y. (2019) · Computing, Communications and IoT Applications (ComComAp) · doi:10.1109/comcomap46287.2019.9018679 · Cited by 1 · see citing works

Determinantal Point Process as an alternative to NMS

Some, S., Gupta, M. D., & Namboodiri, V. P. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.112 · Cited by 1 · see citing works

Looking outside of the Box: Object Detection and Localization with Multi-scale Patterns.Preprint

Ohn-Bar, E., & Trivedi, M. M. (2015) · arXiv (Cornell University) · Cited by 1 · see citing works

WITHDRAWN: Latent hierarchical part based models for road scene understanding

Venkateshkumar, S. K., Sridhar, M., & Ott, P. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.06.001

Iterative Maximum Clique Clustering Based Detection Filter

Zhang, X., Sheng, H., Zhang, Y., Chen, J., Wu, Y., Xue, G., & Wei, Q. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04212-7_13

Research on the object detection in complex scenes using diagonal distance

Guo, D., & He, G. (2025) · IET Image Processing · doi:10.1049/ipr2.13305

An improved YOLOv5s‐CBAM network: For non‐standard operation identification in power network maintenance and operation

Zhang, Z., Li, M., & Guo, W. (2025) · The Journal of Engineering · doi:10.1049/tje2.70039

Image structural analysis and adaptive resonance in artificial neural networks (Review paper)

Lutsiv, V. R., Mikhalkova, M. A., & Yachnaya, V. O. (2025) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2025-25-2-273-285

Conception d'architectures profondes pour l'interprétation de données visuelles

Mordan, T. (2018) · doi:10.70675/7263c670z2517z402dzad8fz1f991ba5d034

End-to-end integration of a convolutional network, deformable parts model and non-maximum suppression. arXivPreprint

Wan, L., Eigen, D., & Fergus, R. (2014)

Deformable part models are convolutional neural networks

Girshick, R., Iandola, F., Darrell, T., & Malik, 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.2015.7298641 · Cited by 423 · see citing works

Deformable Part Models with CNN Features

Savalle, P.-A., Tsogkas, S., Papandreou, G., & Kokkinos, I. (2014) · HAL · Cited by 44 · see citing works

Deformable Part-based Fully Convolutional Network for Object Detection

Mordan, T., Thome, N., Hénaff, G., & Cord, M. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.88 · Cited by 24 · see citing works

Deconstructing the Deformable Parts Model: Do More with Less

Schroeder, B., Sun, B., Saenko, K., & Ali, K. (2015)