R-FCN: Object Detection via Region-based Fully Convolutional Networks

We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image... (read more)

PDF Abstract NeurIPS 2016 PDF NeurIPS 2016 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Real-Time Object Detection PASCAL VOC 2007 R-FCN MAP 80.5% # 2
FPS 9 # 4
Object Detection PASCAL VOC 2007 R-FCN MAP 80.5% # 14

Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
Global Average Pooling
Pooling Operations
1x1 Convolution
Convolutions
ReLU
Activation Functions
Batch Normalization
Normalization
Bottleneck Residual Block
Skip Connection Blocks
Max Pooling
Pooling Operations
Kaiming Initialization
Initialization
Residual Connection
Skip Connections
Residual Block
Skip Connection Blocks
ResNet
Convolutional Neural Networks
Convolution
Convolutions
R-FCN
Object Detection Models
Position-Sensitive RoI Pooling
RoI Feature Extractors