Feature pyramid networks for object detection
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This document discusses feature pyramid networks for object detection. It introduces feature pyramid networks which use a bottom-up pathway to generate feature maps at multiple scales from a convolutional neural network and a top-down pathway that combines high-level and low-level semantic information. It then describes applying feature pyramid networks to region proposal networks and Fast/Faster R-CNN models for object detection and presents experimental results on using feature pyramid networks for region proposal and object detection.
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The document summarizes research on using deep convolutional neural networks for image classification on the ImageNet dataset. It describes collecting a large dataset, using techniques like ReLU activation, local response normalization, overlapping pooling, dropout, and training across multiple GPUs. The results showed the CNN approach enabled more powerful models for image classification compared to previous methods.
Show, attend and tell
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Vgg
This document discusses very deep convolutional networks for large-scale image recognition. It describes network configurations that use 3x3 convolutional filters with max pooling layers and fully connected layers. The networks have 11 or 19 weight layers and use 1x1 convolutional filters to introduce nonlinearity. Classification experiments on ImageNet data with over 1 million training images achieve top-1 and top-5 error rates.
A neural image caption generator
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Feature pyramid networks for object detection
- 1. Feature Pyramid Networks for Object Detection Mar 14, 2021 HeeDae Kwon
- 2. Contents • Introduction • Feature Pyramid Networks • Experiments on Object Dectection
- 4. Feature Pyramid Networks Bottom-up pathway => ConvNet , Define pyramid level Top-down pathway => upsampling
- 5. Application • Feature Pyramid Networks for RPN - single scale feature map => FPN - use shared parameter • Feature Pyramid Networks for Fast-RCNN
- 6. Experiments on Object Dectection • Region Proposal with RPN
- 7. Experiments on Object Dectection • Fast/Faster R-CNN
- 8. • 감사합니다