Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

You Only Look Once: Unified, Real-Time Object Detection

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803

Microsoft COCO: Common Objects in Context

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460

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

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

Focal Loss for Dense Object Detection

Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581

Mask R-CNN

He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137

Fast R-CNN

Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105

SSD: Single Shot MultiBox Detector

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128

A decision-theoretic generalization of on-line learning and an application to boosting

Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466

YOLO9000: Better, Faster, Stronger

Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892

The pascal visual object classes (VOC) challenge

Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599

Robust Real-Time Face Detection

Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097

Aggregated Residual Transformations for Deep Neural Networks

Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.634 · Cited by 12,465

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

Deformable Convolutional Networks

Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513

Cascade R-CNN: Delving Into High Quality Object Detection

Cai, Z., & Vasconcelos, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00644 · Cited by 7,370

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

R-FCN: Object Detection via Region-based Fully Convolutional NetworksPreprint

Dai, J., Li, Y., He, K., & Sun, J. Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06409 · Cited by 5,135

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_23 · Cited by 3,034

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

A convolutional neural network cascade for face detection

Li, H., Lin, Z., Shen, X., Brandt, J., & Hua, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299170 · Cited by 1,390

Perceptual Generative Adversarial Networks for Small Object Detection

Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., & Yan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.211 · Cited by 953 · cited 1×

Robust Object Detection via Soft Cascade

Bourdev, L., & Brandt, J. (2005) · 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.2005.310 · Cited by 329

Attentive Contexts for Object Detection

Li, J., Wei, Y., Liang, X., Dong, J., Xu, T., Feng, J., & Yan, S. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2642789 · Cited by 254

TS$$^{2}$$C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection

Wei, Y., Shen, Z., Cheng, B., Shi, H., Xiong, J., Feng, J., & Huang, T. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_27 · Cited by 140

Towards Computational Baby Learning: A Weakly-Supervised Approach for Object Detection

Liang, X., Liu, S., Wei, Y., Liu, L., Lin, L., & Yan, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.120 · Cited by 110

Deep Regionlets for Object Detection

Xu, H., Lv, X., Wang, X., Ren, Z., Bodla, N., & Chellappa, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_49 · Cited by 75

Multistage Object Detection With Group Recursive Learning

Li, J., Liang, X., Li, J., Wei, Y., Xu, T., Feng, J., & Yan, S. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2772796 · Cited by 59

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

Deformable ConvNets V2: More Deformable, Better Results

Zhu, X., Hu, H., Lin, S., & Dai, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00953 · Cited by 2,943

Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection

Zhang, S., Chi, C., Yao, Y., Lei, Z., & Li, S. Z. (2020) · 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/cvpr42600.2020.00978 · Cited by 2,614

Learning Spatio-Temporal Transformer for Visual Tracking

Yan, B., Peng, H., Fu, J., Wang, D., & Lu, H. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01028 · Cited by 1,280

Scale-Aware Trident Networks for Object Detection

Li, Y., Chen, Y., Wang, N., & Zhang, Z. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00615 · Cited by 1,251

Decoupled Classification Refinement: Hard False Positive Suppression for Object DetectionPreprint

Cheng, B., Wei, Y., Feris, R., Xiong, J., Hwu, W., Huang, T. S., & Shi, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.04002 · Cited by 46

Reducing false positives for object detection

Cheng, B. (2019) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)

G-RCN: Optimizing the Gap between Classification and Localization Tasks for Object DetectionPreprint

Luo, Y., & Xiao, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.03677 · Cited by 2

Single shot object detection with refined feature

Zhang, X., Wang, C., Cheng, L., Jiang, S., & Qi, J. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09483-4 · Cited by 1

ITDRCNN: An Interactive Task-Decoupled RCNN for enhanced object detection

Wu, S., Wei, H., Quan, Y., Xu, Y., Tian, C., & Miao, Q. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114190