DetNet: Design Backbone for Object Detection

Recent CNN based object detectors, either one-stage methods like YOLO, SSD, and RetinaNet, or two-stage detectors like Faster R-CNN, R-FCN and FPN, are usually trying to directly finetune from ImageNet pre-trained models designed for the task of image classification. However, there has been little work discussing the backbone feature extractor specifically designed for the task of object detection... (read more)

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Methods used in the Paper


METHOD TYPE
RPN
Region Proposal
Dilated Convolution
Convolutions
ReLU
Activation Functions
RoIPool
RoI Feature Extractors
Softmax
Output Functions
Residual Connection
Skip Connections
Faster R-CNN
Object Detection Models
Average Pooling
Pooling Operations
Dilated Bottleneck with Projection Block
Skip Connection Blocks
Dilated Bottleneck Block
Skip Connection Blocks
Non Maximum Suppression
Proposal Filtering
Global Average Pooling
Pooling Operations
SSD
Object Detection Models
DetNet
Convolutional Neural Networks
Focal Loss
Loss Functions
1x1 Convolution
Convolutions
FPN
Feature Extractors
RetinaNet
Object Detection Models
Position-Sensitive RoI Pooling
RoI Feature Extractors
Convolution
Convolutions
R-FCN
Object Detection Models