The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
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“In the deep learning era, due to the increase of computing power, bootstrap was shortly discarded in object detection during 2014-2016 [16-20].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [17]
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“and so, if exploited, image pyramids are used only at test time [15, 11, 16, 35], which creates an inconsistency between train/test-time inference.”
From Feature Pyramid Networks for Object Detection (2017) · cites this paper as [15]
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“SPPNet is more than 20 times faster than R-CNN without sacrificing any detection accuracy (VOC07 mAP=59.2%).”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [17]
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Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
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Self Paced Deep Learning for Weakly Supervised Object Detection
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A deep 3D residual CNN for false‐positive reduction in pulmonary nodule detection
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PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices
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“, The two-stage detectors[12, 13, 14, 15, 16, 17] which are normally anchor-based, generate region proposals from the image and then generate the final bounding box from the region proposals.”
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Towards Real-Time Object Detection on Embedded Systems
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“In the original paper, DSP-FWA further improves the FWA algorithm by including a spatial pyramid pooling (SPP) module [15] to better handle the variations in the resolutions of the original target faces.”
MethodObject Detection with Transformers: A Review
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“However, the challenge is that two-stage based algorithms, such as R-CNN [18], SPP-Net [19], Fast R-CNN [20], and Faster R-CNN [17], are always too”
MethodObject Localization Based on Proposal Fusion
Tang, S., Li, Y., Deng, L., & Zhang, Y. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2729786 · Cited by 25
We Don’t Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification
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Weakly- and Semi-Supervised Fast Region-Based CNN for Object Detection
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Metadata
- Authors
- He, K., Zhang, X., Ren, S., & Sun, J.
- Affiliations
- Microsoft Research Asia (China) (CN)
- Xi'an Jiaotong University (CN)
- University of Science and Technology of China (CN)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2014
- Pages
- 346-361
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2179352600
- Cited by
- 3,034
- References
- 53
- Citations by year
- 2026: 43 · 2025: 113 · 2024: 211 · 2023: 286 · 2022: 279 · 2021: 306 · 2020: 280 · 2019: 423 · 2018: 373 · 2017: 324 · 2016: 218 · 2015: 152 · 2014: 25
- FWCI
- 128.86
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- 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 › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- spatial pyramid pooling, image classification, object detection, feature pooling, computational efficiency
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