A single-shot multi-level feature reused neural network for object detection
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Zero-shot learning is a supervised learning approach that opens applications such as object detection, object classification and retrieval of videos, especially when appropriate datasets are missing.
“This is why zero-shot learning [130] takes place. This supervised learning approach opens up many valuable applications such as object detection [131], object classification and retrieval of videos [141], especially when appropriate datasets are missing.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [131]
Cites
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 · see citing works
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 56,035 · 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
(2016) · Cited by 31,590 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015 · see citing works
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141 · see citing works
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971 · see citing works
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
(2017) · doi:10.1109/cvpr35066.2017 · Cited by 7,281 · 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
International Conference on Learning Representations (ICLR 2013)
松尾, 豊. (2013) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 6,286 · see citing works
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 · see citing works
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works
Random Erasing Data Augmentation
Zhong, Z., Zheng, L., Kang, G., Li, S., & Yang, Y. (2017) · arXiv · doi:10.48550/arxiv.1708.04896 · Cited by 3,617 · see citing works
Random Erasing Data Augmentation
Zhong, Z., Zheng, L., Kang, G., Li, S., & Yang, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.7000 · Cited by 3,617 · see citing works
2017 IEEE International Conference on Computer Vision (ICCV)
(2017) · doi:10.1109/iccv37128.2017 · Cited by 3,129 · see citing works
One-shot learning of object categories
Fei-Fei, L., Fergus, R., & Perona, P. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.79 · Cited by 3,029 · see citing works
Soft-NMS — Improving Object Detection with One Line of Code
Bodla, N., Singh, B., Chellappa, R., & Davis, L. S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.593 · Cited by 2,274 · cited 1× · see citing works
“And Soft-nms [30], which has the same algorithmic complexity as traditional NMS, is deployed to effectively reduce false positive test results.”
BackgroundReceptive Field Block Net for Accurate and Fast Object Detection
Liu, S., Huang, D., & Wang, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_24 · Cited by 1,885 · see citing works
DSSD : Deconvolutional Single Shot Detector
Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., & Berg, A. C. (2017) · arXiv · doi:10.48550/arxiv.1701.06659 · Cited by 1,624 · see citing works
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
Cai, Z., Fan, Q., Feris, R. S., & Vasconcelos, N. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_22 · Cited by 1,605 · see citing works
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
Bell, S., Zitnick, C. L., Bala, K., & Girshick, R. (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.2016.314 · Cited by 1,344 · see citing works
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017) · arXiv · doi:10.48550/arxiv.1708.02002 · Cited by 1,227 · see citing works
An Embarrassingly Simple Approach to Zero-Shot Learning
Romera‐Paredes, B., & Torr, P. H. S. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-50077-5_2 · Cited by 1,113 · see citing works
International Conference on Machine Learning (ICML-99).
Scheffer, T. (1999) · Künstliche Intell. · Cited by 1,011 · see citing works
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
Kong, T., Yao, A., Chen, Y., & Sun, F. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.98 · Cited by 934 · see citing works
An Analysis of Scale Invariance in Object Detection - SNIP
Singh, B., & Davis, L. S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00377 · Cited by 913 · see citing works
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
Gidaris, S., & Komodakis, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.135 · Cited by 801 · see citing works
Label-Embedding for Image Classification
Akata, Z., Perronnin, F., Harchaoui, Z., & Schmid, C. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2487986 · Cited by 785 · see citing works
Synthesized Classifiers for Zero-Shot Learning
Changpinyo, S., Chao, W., Gong, B., & Sha, F. (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.2016.575 · Cited by 720 · see citing works
Reclik, T. M. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.13270696 · Cited by 599 · see citing works
Computer Vision and Pattern Recognition: CVPR 92
Brill, M. H. (1992) · Color Research & Application · doi:10.1002/col.5080170616 · Cited by 574 · see citing works
A Bi-Directional Message Passing Model for Salient Object Detection
Zhang, L., Dai, J. P., Lu, H., He, Y., & Wang, G. (2018) · 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.2018.00187 · Cited by 517 · see citing works
RON: Reverse Connection with Objectness Prior Networks for Object Detection
Kong, T., Sun, F., Yao, A., Liu, H., Lu, M., & Chen, 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.2017.557 · Cited by 452 · see citing works
Enhancement of SSD by concatenating feature maps for object detection
Jeong, J., Park, H., & Kwak, N. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.76 · Cited by 394 · see citing works
Scale-Transferrable Object Detection
Zhou, P., Ni, B., Geng, C., Hu, J., & Xu, Y. (2018) · 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.2018.00062 · Cited by 391 · see citing works
Bansal, A., Sikka, K., Sharma, G., Chellappa, R., & Divakaran, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_24 · Cited by 378 · see citing works
Attentive Region Embedding Network for Zero-Shot Learning
Xie, G.-S., Liu, L., Jin, X., Zhu, F., Zhang, Z., Qin, J., Yao, Y., & Shao, L. (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.2019.00961 · Cited by 332 · see citing works
SANet: Structure-Aware Network for Visual Tracking
Fan, H., & Ling, H. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.275 · Cited by 263 · see citing works
Weakly-Shared Deep Transfer Networks for Heterogeneous-Domain Knowledge Propagation
Shu, X., Qi, G.-J., Tang, J., & Wang, J. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806216 · Cited by 218 · see citing works
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
Rahman, S., Khan, S. H., & Porikli, F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20887-5_34 · Cited by 161 · see citing works
Contextual Priming and Feedback for Faster R-CNN
Shrivastava, A., & Gupta, A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_20 · Cited by 153 · see citing works
Generalized Deep Transfer Networks for Knowledge Propagation in Heterogeneous Domains
Tang, J., Shu, X., Li, Z., Qi, G.-J., & Wang, J. (2016) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2998574 · Cited by 143 · see citing works
Exploiting Web Images for Dataset Construction: A Domain Robust Approach
Yao, Y., Zhang, J., Shen, F., Hua, X., Xu, J., & Tang, Z. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2684626 · Cited by 98 · see citing works
DSOD: Learning Deeply Supervised Object Detectors from Scratch
Shen, Z., Liu, Z., Li, J., et al. (2017) · arXiv · doi:10.48550/arxiv.1708.01241 · Cited by 94 · see citing works
Recognize Actions by Disentangling Components of Dynamics
Zhao, Y., Xiong, Y., & Lin, D. (2018) · 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.2018.00687 · Cited by 90 · see citing works
Towards Automatic Construction of Diverse, High-Quality Image Datasets
Yao, Y., Zhang, J., Shen, F., Liu, L., Zhu, F., Zhang, D., & Shen, H. T. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2903036 · Cited by 82 · see citing works
Visual recognition by learning from web data: A weakly supervised domain generalization approach
Niu, L., Li, W., & Xu, D. (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.2015.7298894 · Cited by 78 · see citing works
Extracting Privileged Information for Enhancing Classifier Learning
Yao, Y., Shen, F., Zhang, J., Liu, L., Tang, Z., & Shao, L. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2869721 · Cited by 59 · see citing works
RGB-D Object Recognition via Incorporating Latent Data Structure and Prior Knowledge
Tang, J., Jin, L., Li, Z., & Gao, S. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2476660 · Cited by 58 · see citing works
Cancelable multi-biometric recognition system based on deep learning
Abdellatef, E., Ismail, N. A., Abdulrahman, S. E. S. E., et al. (2019) · The Visual Computer · doi:10.1007/s00371-019-01715-5 · Cited by 43 · see citing works
Multimodal Neural Networks: RGB-D for Semantic Segmentation and Object Detection
Schneider, L., Jasch, M., Fröhlich, B., Weber, T., Franke, U., Pollefeys, M., & Rätsch, M. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59126-1_9 · Cited by 42 · see citing works
3D RANs: 3D Residual Attention Networks for action recognition
Cai, J., & Hu, J. (2019) · The Visual Computer · doi:10.1007/s00371-019-01733-3 · Cited by 36 · see citing works
Automatic image dataset construction with multiple textual metadata
Yao, Y., Zhang, J., Shen, F., Hua, X., Xu, J., & Tang, Z. (2016) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2016.7552988 · Cited by 29 · see citing works
Zero-Shot Object Detection by Hybrid Region EmbeddingPreprint
Demirel, B., Cinbiş, R. G., & İkizler-Cinbiş, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.06157 · Cited by 18 · see citing works
ME R-CNN: Multi-Expert Region-based CNN for Object Detection.Preprint
Lee, H., Eum, S., & Kwon, H. (2017) · arXiv (Cornell University) · Cited by 17 · see citing works
A novel optimization framework for salient object detection
Zhang, H., Xu, M. G., Zhuo, L., et al. (2014) · The Visual Computer · doi:10.1007/s00371-014-1053-z · Cited by 15 · see citing works
Zero-Shot Learning via Joint Latent Similarity EmbeddingPreprint
Zhang, Z., & Saligrama, V. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04512 · Cited by 13 · see citing works
Discriminative Hough context model for object detection
Li, T., Ye, M., & Ding, J. (2013) · The Visual Computer · doi:10.1007/s00371-013-0780-x · Cited by 12 · see citing works
End-to-end deep metric network for visual tracking
Tian, S., Shen, S., Tian, G., et al. (2019) · The Visual Computer · doi:10.1007/s00371-019-01730-6 · Cited by 12 · see citing works
How do deep convolutional features affect tracking performance: an experimental study
Guan, H., & Cheng, B. (2017) · The Visual Computer · doi:10.1007/s00371-017-1445-y · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works
- Authors
- Wei, L., Cui, W., Hu, Z., Sun, H., & Hou, S.
- Affiliations
- Yanshan University (CN)
- Corresponding author
- Cui, Wei
- Venue
- The Visual Computer
- Published
- 2020-01-03
- Volume
- 37
- Issue
- 1
- Pages
- 133-142
- ISSN
- 0178-2789 (ISSN-L)
- 1432-2315
- OpenAlex
- W2998731096
- Cited by
- 29
- References
- 63
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 5 · 2023: 6 · 2022: 10 · 2021: 3 · 2020: 2
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Visual Computer (publishedVersion)
- 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
- object detection, single-shot detection, lightweight neural networks, computational efficiency, Pascal VOC 2007
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
