YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
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The SCDown module reduces computational cost to O(2HWC2+9/2HWC) and the number of parameters to O(2C2+18C).
“The SCDown module is a module proposed in YoloV10 [22] for feature extraction, which reduces the number of channels by a 1 × 1 convolutional layer on the input tensor X∈RC1×H×W. The 3 × 3 deep convolutional layers perform convolutional operations independently within each channel, reducing the computational cost to O(2HWC2+9/2HWC). The number of parameters is reduced to O(2C2+18C).”
From Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning (2024) · cites this paper as [22]
YOLO-NAS was developed by Deci AI before YOLOv9.
“Before the introduction of YOLOv9 [132], YOLO-NAS [133] developed by Deci AI marked a significant shift in the evolution of the YOLO framework.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [132]
PGI ensured smoother backpropagation across multiple prediction branches, improving convergence and overall detection accuracy.
“YOLOv9's PGI ensured smoother backpropagation across multiple prediction branches, significantly improving convergence and overall detection accuracy.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [132]
PGI allowed YOLOv9 to achieve better performance in detecting multiple objects in challenging environments.
“By addressing gradient backpropagation across complex prediction branches, PGI allowed YOLOv9 to achieve better performance, particularly in detecting multiple objects in challenging environments.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [132]
YOLOv9 introduced Programmable Gradient Information (PGI) to tackle vanishing gradients by enhancing gradient flow.
“Programmable Gradient Information (PGI): PGI was designed to tackle the issue of vanishing gradients by enhancing the flow of gradients throughout the model.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [132]
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
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Self-citationYOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors
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Self-citationFCOS: Fully Convolutional One-Stage Object Detection
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Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
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DETRs Beat YOLOs on Real-time Object Detection
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Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
Multi-Object Vehicle Detection and Tracking Algorithm Based on Improved YOLOv8 and ByteTrack
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Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models
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Edge-Constrained Video Analytics: A Comprehensive Survey on Detection, Tracking, and Recognition
Toshpulatov, M. A., Lee, W., & Cho, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6614394
- Authors
- Wang, C.-Y., Yeh, I.-H., & Mark Liao, H.-Y.
- Affiliations
- National Taipei University of Technology (TW)
- Corresponding author
- Wang, Chien-Yao
- ORCID
- Wang, Chien-Yao
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024-10-26
- Pages
- 1-21
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4403770406
- Cited by
- 4,823
- References
- 16
- Citations by year
- 2026: 1515 · 2025: 2460 · 2024: 848
- FWCI
- 995.57
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- 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 › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- YOLOv9, information bottleneck, lightweight neural networks, object detection, convolutional neural networks, parameter efficiency
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