Cited by 282Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval TechniquesVisual Attention and Saliency Detection

The DOI matches its Crossref record; 32 of the 34 references it cites were found in a registry or an index.

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

Open access

Cheng, Bowen, Wei, Yunchao, Shi, Honghui, Feris, Rogerio, Xiong, Jinjun, Huang, Thomas·2018·Lecture Notes in Computer Science

DOI 10.1007/978-3-030-01267-0_28

Open this paper in CoChat — free account Open PDF

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1007/978-3-030-01267-0_28

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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

Show all 34 references

Research works printed but not linked: 2

Fu, C.Y., Liu, W., Ranga, A., Tyagi, A., Berg, A.C.: DSSD: deconvolutional single shot detector. arXiv preprint arXiv:1701.06659 (2017) · arXiv
Goyal, P., et al.: Accurate, large minibatch SGD: training imagenet in 1 hour. arXiv preprint arXiv:1706.02677 (2017) · arXiv

Sorted by citations

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
Mask Scoring R-CNN
Huang, Z., Huang, L., Gong, Y., Huang, C., & Wang, X. (2019) · 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.2019.00657 · Cited by 1,209
Recent advances in deep learning for object detection
Wu, X., Sahoo, D., & Hoi, S. C. H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.085 · Cited by 1,047
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T. S., & Zhang, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00543 · Cited by 1,023
Self-citation
Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
Zhang, H., Chang, H., Ma, B., Wang, N., & Chen, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_16 · Cited by 735
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
Cheng, B., Collins, M. D., Zhu, Y., Liu, T., Huang, T. S., Adam, H., & Chen, L.-C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01249 · Cited by 635
Self-citation
A review of object detection based on deep learning
Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., & Lan, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08976-6 · Cited by 584
Revisiting the Sibling Head in Object Detector
Song, G., Liu, Y., & Wang, X. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01158 · Cited by 509
DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection
Qiao, L., Zhao, Y., Li, Z., Qiu, X., Wu, J., & Zhang, C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00856 · Cited by 440
Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review
Amjoud, A. B., & Amrouch, M. (2023) · IEEE Access · doi:10.1109/access.2023.3266093 · Cited by 389
Prime Sample Attention in Object Detection
Cao, Y., Chen, K., Loy, C. C., & Lin, D. (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/cvpr42600.2020.01160 · Cited by 258
Self-produced Guidance for Weakly-Supervised Object Localization
Zhang, X., Wei, Y., Kang, G., Yang, Y., & Huang, T. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_37 · Cited by 248
Self-citation
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
A comprehensive and systematic look up into deep learning based object detection techniques: A review
Sharma, V., & Mir, R. N. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100301 · Cited by 230
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 224
D2Det: Towards High Quality Object Detection and Instance Segmentation
Cao, J., Cholakkal, H., Anwer, R. M., Khan, F. S., Pang, Y., & Shao, L. (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/cvpr42600.2020.01150 · Cited by 210
CornerNet-Lite: Efficient Keypoint based Object Detection
Law, H., Teng, Y. T., Russakovsky, O., & Deng, J. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.5 · Cited by 224
Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review
Singh, M., Kumar, A., Khanna, N. N., Laird, J. R., Nicolaides, A. N., Faa, G., Johri, A. M., Mantella, L. E., Fernandes, J. F. E., Teji, J. S., et al. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102660 · Cited by 197
End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction
Saha, A., Hosseinzadeh, M., & Huisman, H. J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102155 · Cited by 175
ScratchDet: Training Single-Shot Object Detectors From Scratch
Zhu, R., Zhang, S., Wang, X., Wen, L., Shi, H., Bo, L., & Mei, T. (2019) · 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.2019.00237 · Cited by 159
Self-citation
NoduleNet: Decoupled False Positive Reduction for Pulmonary Nodule Detection and Segmentation
Tang, H., Zhang, C., & Xie, X. S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32226-7_30 · Cited by 154
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
Self-citation
LIP: Local Importance-Based Pooling
Gao, Z., Wang, L., & Wu, G. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00345 · Cited by 141
RefineDet++: Single-Shot Refinement Neural Network for Object Detection
Zhang, S., Wen, L., Lei, Z., & Li, S. Z. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2986402 · Cited by 126
Learning From Noisy Anchors for One-Stage Object Detection
Li, H., Wu, Z., Zhu, C. G., Xiong, C., Socher, R., & Davis, L. S. (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/cvpr42600.2020.01060 · Cited by 118
Bi-Directional Interaction Network for Person Search
Dong, W., Zhang, Z., Song, C., & Tan, T. (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/cvpr42600.2020.00291 · Cited by 115
The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale
Ertler, C., Mislej, J., Ollmann, T., Porzi, L., Neuhold, G., & Kuang, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58592-1_5 · Cited by 120
Few-Shot Object Detection via Classification Refinement and Distractor Retreatment
Li, Y., Zhu, H.-Y., Cheng, Y., Wang, W.-X., Teo, C.-S., Xiang, C., Vadakkepat, P., & Lee, T. (2021) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr46437.2021.01514 · Cited by 92
Multi-model ensemble with rich spatial information for object detection
Xu, J., Wang, W., Wang, H., & Guo, J. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107098 · Cited by 96
Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation
Khirodkar, R., Chari, V., Agrawal, A., & Tyagi, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00311 · Cited by 100
AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts
Chen, T., Chen, X., Du, X., Rashwan, A. M., Yang, F., Chen, H., Wang, Z., & Li, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01591 · Cited by 101
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded SystemsPreprint
Zhang, X., Lu, H., Hao, C., Li, J., Cheng, B., Li, Y., Rupnow, K., Xiong, J., Huang, T. S., Shi, H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.09709 · Cited by 91
Self-citation
Comprehensive review of recent developments in visual object detection based on deep learning
Edozie, E., Shuaibu, A. N., John, U. K., & Sadiq, B. O. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11284-w · Cited by 100
Automated identification of citrus diseases in orchards using deep learning
Zhang, X., Xun, Y., & Chen, Y. (2022) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2022.09.006 · Cited by 89
Occluded Human Mesh Recovery
Khirodkar, R., Tripathi, S., & Kitani, K. (2022) · 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/cvpr52688.2022.00176 · Cited by 88
AP-Loss for Accurate One-Stage Object Detection
Chen, K., Lin, W., Li, J., See, J., Wang, J., & Zou, J. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2991457 · Cited by 87
FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions
Mao, W., Tian, Z., Wang, X., & Shen, C. (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/cvpr46437.2021.00892 · Cited by 84
Oriented object detection in optical remote sensing images using deep learning: a survey
Wang, K., Wang, Z., Li, Z., Su, A., Teng, X., Pan, E., Liu, M., & Yu, Q. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11256-0 · Cited by 103
High-Quality R-CNN Object Detection Using Multi-Path Detection Calibration Network
Chen, X., Li, H., Wu, Q., Ngan, K. N., & Xu, L. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2987465 · Cited by 83
Context-Aware RCNN: A Baseline for Action Detection in Videos
Wu, J.-C., Kuang, Z., Wang, L., Zhang, W., & Wu, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58595-2_27 · Cited by 83
Transformer-Based Decoder Designs for Semantic Segmentation on Remotely Sensed Images
Panboonyuen, T., Jitkajornwanich, K., Lawawirojwong, S., Srestasathiern, P., & Vateekul, P. (2021) · Remote Sensing · doi:10.3390/rs13245100 · Cited by 80
EfficientHRNet
Neff, C., Sheth, A., Furgurson, S., Middleton, J., & Tabkhi, H. (2021) · Journal of Real-Time Image Processing · doi:10.1007/s11554-021-01132-9 · Cited by 76
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
Soft Error Resilience of Deep Residual Networks for Object Recognition
Ibrahim, Y., Wang, H., Bai, M., Liu, Z., Wang, J., Yang, Z., & Chen, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2968129 · Cited by 72
MS-FRCNN: A Multi-Scale Faster RCNN Model for Small Target Forest Fire Detection
Zhang, L., Wang, M., Ding, Y., & Bu, X. (2023) · Forests · doi:10.3390/f14030616 · Cited by 70
Cascade RetinaNet: Maintaining Consistency for Single-Stage Object DetectionPreprint
Zhang, H., Chang, H., Ma, B., Shan, S., & Chen, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.06881 · Cited by 67
Decouple Learning for Parameterized Image Operators
Fan, Q., Chen, D., Yuan, L., Hua, G., Yu, N., & Chen, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_27 · Cited by 65
Garbage Detection using Advanced Object Detection Techniques
Patel, D., Patel, F., Patel, S., Patel, N., Shah, D., & Patel, V. D. (2021) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais50930.2021.9395916 · Cited by 55
Multi-task Generative Adversarial Network for Detecting Small Objects in the Wild
Zhang, Y., Bai, Y., Ding, M., & Ghanem, B. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01301-6 · Cited by 50
Edge Computing-Driven Real-Time Drone Detection Using YOLOv9 and NVIDIA Jetson Nano
Hakani, R., & Rawat, A. (2024) · Drones · doi:10.3390/drones8110680 · Cited by 54
Mutual Supervision for Dense Object Detection
Gao, Z., Wang, L., & Wu, G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00362 · Cited by 47
Diverse Knowledge Distillation for End-to-End Person Search
Zhang, X., Wang, X., Bian, J.-W., Shen, C., & You, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16454 · Cited by 44
BBC Net: Bounding-Box Critic Network for Occlusion-Robust Object Detection
Kim, J. U., Kwon, J., Kim, H. G., & Ro, Y. M. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2900709 · Cited by 43
Multi-Class Cell Detection Using Spatial Context Representation
Abousamra, S., Belinsky, D., Arnam, J. V., Allard, F. D., Yee, E. U., Gupta, R., Kurç, T., Samaras, D., Saltz, J. H., & Chen, C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00397 · Cited by 45
Missing Labels in Object Detection
Xu, M., Bai, Y., & Ghanem, B. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · Cited by 42
RLDD-YOLOv11n: Research on Rice Leaf Disease Detection Based on YOLOv11
Fang, K., Zhou, R. H., Deng, N. S., Li, C., & Zhu, X. (2025) · Agronomy · doi:10.3390/agronomy15061266 · Cited by 43
DCL-Net: Augmenting the Capability of Classification and Localization for Remote Sensing Object Detection
Liu, E., Zheng, Y., Pan, B., Xu, X., & Shi, Z. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3048384 · Cited by 40
Enhancing autonomous pavement crack detection: Optimizing YOLOv5s algorithm with advanced deep learning techniques
Zhou, S., Yang, D., Zhang, Z., Zhang, J., Qu, F., Punetha, P., Li, W., & Li, N. (2024) · Measurement · doi:10.1016/j.measurement.2024.115603 · Cited by 39
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
Self-citation
MARN: Multi-Scale Attention Retinex Network for Low-Light Image Enhancement
Zhang, X., & Wang, X. (2021) · IEEE Access · doi:10.1109/access.2021.3068534 · Cited by 37
CrabNet: Fully Task-Specific Feature Learning for One-Stage Object Detection
Wang, H., Wang, Q., Zhang, H., Hu, Q., & Zuo, W. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3162099 · Cited by 38
Decoupled R-CNN: Sensitivity-Specific Detector for Higher Accurate Localization
Wang, D., Shang, K., Wu, H., & Wang, C. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3167114 · Cited by 38
Learning to Acquire the Quality of Human Pose Estimation
Zhao, L., Xu, J., Gong, C., Yang, J. J., Zuo, W., & Gao, X. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3005522 · Cited by 34
The 1st Tiny Object Detection Challenge: Methods and Results
Yu, X., Han, Z., Gong, Y., Jan, N., Zhao, J. C., Ye, Q., Chen, J., Feng, Y., Zhang, B., Wang, X., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-68238-5_23 · Cited by 34
Self-citation
Improved Selective Refinement Network for Face DetectionPreprint
Zhang, S., Zhu, R., Wang, X., Shi, H., Fu, T., Wang, S., Mei, T., & Li, S. Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.06651 · Cited by 34
Self-citation
An adaptive ensemble deep learning framework for reliable detection of pandemic patients
Iqbal, M. S., Naqvi, R. A., Alizadehsani, R., Hussain, S., Moqurrab, S. A., & Lee, S. W. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107836 · Cited by 33
Bottom-up Higher-Resolution Networks for Multi-Person Pose EstimationPreprint
Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T. S., & Zhang, L. (2019) · arXiv (Cornell University) · Cited by 33
Self-citation
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection
Xu, H., Lv, X., Wang, X., Ren, Z., Bodla, N., & Chellappa, R. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2957780 · Cited by 34
Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection
Li, J., Cheng, B., Feris, R., Xiong, J., Huang, T. S., Hwu, W., & Shi, H. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00270 · Cited by 35
Self-citation
A Progressive Approach to Generic Object Detection: A Two-Stage Framework for Image Recognition
Aamir, M. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.038173 · Cited by 31
Simultaneous Segmentation and Classification of Pressure Injury Image Data Using Mask‐R‐CNN
Swerdlow, M., Güler, Ö., Yaakov, R. A., & Armstrong, D. G. (2023) · Computational and Mathematical Methods in Medicine · doi:10.1155/2023/3858997 · Cited by 31
Detecting Floodwater on Roadways from Image Data Using Mask-R-CNN
Sarp, S., Kuzlu, M., Cetin, M., Sazara, C., & Güler, Ö. (2020) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista49547.2020.9194655 · Cited by 30
YOLOv7oSAR: A Lightweight High-Precision Ship Detection Model for SAR Images Based on the YOLOv7 Algorithm
Liu, Y., Ma, Y., Chen, F., Shang, E., Yao, W., Zhang, S., & Yang, J. (2024) · Remote Sensing · doi:10.3390/rs16050913 · Cited by 30
YOLO-OD: Obstacle Detection for Visually Impaired Navigation Assistance
Wang, W., Jing, B., Yu, X., Sun, Y., Yang, L., & Wang, C. (2024) · Sensors · doi:10.3390/s24237621 · Cited by 29
Decoupled IoU Regression for Object Detection
Gao, Y., Wang, Q., Tang, X., Wang, H., Ding, F., Li, J., & Hu, Y. G. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475707 · Cited by 29
YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance
Wang, W., Yu, X., Jing, B., Tang, Z., Zhang, W., Wang, S., Xiao, Y., Li, S., & Yang, L. (2025) · Sensors · doi:10.3390/s25051442 · Cited by 27
Pose Anchor: A Single-Stage Hand Keypoint Detection Network
Li, Y., Wang, X., Liu, W., & Feng, B. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2912620 · Cited by 27
Abandoned Object Detection and Classification Using Deep Embedded Vision
Qasim, A. M., Abbas, N., Ali, A., & Al-Ghamdi, B. A. M. A.-R. (2024) · IEEE Access · doi:10.1109/access.2024.3369233 · Cited by 26
Learning-based needle tip tracking in 2D ultrasound by fusing visual tracking and motion prediction
Yan, W., Ding, Q., Chen, J., Yan, K., Tang, R. S. Y., & Cheng, S. S. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102847 · Cited by 26
Robust one-stage object detection with location-aware classifiers
Chen, Q., Wang, P., Cheng, A., Wang, W., Zhang, Y., & Cheng, J. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107334 · Cited by 26
Automated detection of glottic laryngeal carcinoma in laryngoscopic images from a multicentre database using a convolutional neural network
Yan, P., Li, S., Zhou, Z., Liu, Q., Wu, J., Ren, Q., Chen, Q., Chen, Z., Chen, Z., Chen, S., et al. (2023) · Clinical Otolaryngology · doi:10.1111/coa.14029 · Cited by 25
Lightweight Super-Resolution Head for Human Pose Estimation
Wang, H., Liu, J., Tang, J., & Wu, G. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612236 · Cited by 26
Foreground Fusion-Based Liquefied Natural Gas Leak Detection Framework From Surveillance Thermal Imaging
Bin, J., Bahrami, Z., Rahman, C. A., Du, S., Rogers, S., & Liu, Z. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3214826 · Cited by 24
Scale‐aware limited deformable convolutional neural networks for traffic sign detection and classification
Liu, Z., Shen, C., Fan, X., Zeng, G., & Zhao, X. (2020) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2020.0217 · Cited by 25
A Region-Based Efficient Network for Accurate Object Detection
Guan, Y., Aamir, M., Hu, Z., Abro, W. A., Rahman, Z.-U., Dayo, Z. A., & Akram, S. (2021) · Traitement du signal · doi:10.18280/ts.380228 · Cited by 23
Occluded Pedestrian Detection via Distribution-Based Mutual-Supervised Feature Learning
He, Y., Zhu, C., & Yin, X.-C. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3094800 · Cited by 23
Efficient Decoder-Free Object Detection with Transformers
Chen, P., Zhang, M., Shen, Y., Sheng, K., Gao, Y., Sun, X., Li, K., & Shen, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_5 · Cited by 23
Object Detection of Road Assets Using Transformer-Based YOLOX with Feature Pyramid Decoder on Thai Highway Panorama
Panboonyuen, T., Thongbai, S., Wongweeranimit, W., Santitamnont, P., Suphan, K., & Charoenphon, C. (2021) · Information · doi:10.3390/info13010005 · Cited by 23
Small Foreign Metal Objects Detection in X-Ray Images of Clothing Products Using Faster R-CNN and Feature Pyramid Network
Gao, R., Sun, Z., Huyan, J., Li, W., Xiao, L., Yao, B., & Wang, H. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3077666 · Cited by 22
High Frequency Residual Learning for Multi-Scale Image ClassificationPreprint
Cheng, B., Xiao, R., Wang, J., Huang, T. S., & Zhang, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.02649 · Cited by 24
Self-citation
Spatio-temporal hierarchical feature transformer for UAV object tracking
Zhu, F., Cui, J., & Dou, K. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.09.016 · Cited by 21
YOLOv9-YX: lightweight algorithm for underwater target detection
Cen, Q., Zhu, Q., Wang, Y., Chen, W., & Liu, S. (2024) · The Visual Computer · doi:10.1007/s00371-024-03644-4 · Cited by 21
Smoke and Fire-You Only Look Once: A Lightweight Deep Learning Model for Video Smoke and Flame Detection in Natural Scenes
Zhao, C., Zhao, L., Ka, Z., Ren, Y., Chen, H., & Sheng, Y. (2025) · Fire · doi:10.3390/fire8030104 · Cited by 21
Pest recognition in microstates state: an improvement of YOLOv7 based on Spatial and Channel Reconstruction Convolution for feature redundancy and vision transformer with Bi-Level Routing Attention
He, J., Zhang, S., Yang, C., Wang, H., Gao, J., Huang, W., Wang, Q., Wang, X., Yuan, W., Wu, Y., et al. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1327237 · Cited by 21
TIR-Net: Task Integration Based on Rotated Convolution Kernel for Oriented Object Detection in Aerial Images
Li, H., Pan, R., Liu, G., Dang, M., Xu, Q., Wang, X., & Wan, B. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3412167 · Cited by 20
Geometry-Aware Traffic Flow Analysis by Detection and Tracking
Shi, H., Wang, Z., Zhang, Y., Wang, X., & Huang, T. S. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00023 · Cited by 19
Self-citation

Authors: Cheng, B., Wei, Y., Shi, H., Feris, R., Xiong, J., & Huang, T.
Affiliations: University of Illinois Urbana-Champaign (US)
IBM (United States) (US)
Corresponding author: Wei, Yunchao
ORCID: Cheng, Bowen
Wei, Yunchao
Xiong, Jinjun
Huang, Thomas
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2018
Pages: 473-490
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-030-01267-0_28
OpenAlex: W2962731685
Cited by: 282
References: 34
Citations by year: 2026: 8 · 2025: 33 · 2024: 39 · 2023: 36 · 2022: 28 · 2021: 52 · 2020: 43 · 2019: 32 · 2018: 11
FWCI: 10.40
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Lecture notes in computer science (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords: Faster R-CNN, region-based object detection, shared feature representation, multi-task learning, small object detection, receptive fields, object classification

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks