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> ML_LITERATURE // REN-2015-FASTER-R-CNN-TOWARDS-REAL-TIME-OBJECT-DETECTION_v1.0

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

Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun · Advances in Neural Information Processing Systems (NeurIPS) (2015)

seminal-architecture2015industry-standardthirdPartyReproduced
arXiv:1506.01497Back to Literature

Principal Contribution

Introduced the Region Proposal Network (RPN) sharing full-image convolutional features with the detection network, enabling near cost-free region proposals and 5 FPS speed.

Operational Relevance

Serves as qualified reference for implementing task-object-detection in production systems.

Assumptions

  • Underlying spatio-temporal continuity and domain distributional stability hold

Limitations

  • Performance scaling and computational footprint depend on receptive field depth, sequence length, and resolution

Connected Algorithms, Architectures & Tools

Related Algorithms:
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Implementing Libraries: