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Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

Hao Yu
Zhaoning Zhang
Zheng Qin
Hao Wu
Dongsheng Li
Jun Zhao
Xicheng Lu
    ObjD
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Papers citing "Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors"

5 / 5 papers shown
Title
Focal Loss for Dense Object Detection
Focal Loss for Dense Object Detection
Nayeon Lee
Priya Goyal
Ross B. Girshick
Kaiming He
Piotr Dollár
ObjD
132
3,000
0
07 Aug 2017
Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure
Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure
Min Wang
Baoyuan Liu
H. Foroosh
57
51
0
15 Aug 2016
R-FCN: Object Detection via Region-based Fully Convolutional Networks
R-FCN: Object Detection via Region-based Fully Convolutional Networks
Jifeng Dai
Yi Li
Kaiming He
Jian Sun
ObjD
185
5,642
0
20 May 2016
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
F. Iandola
Song Han
Matthew W. Moskewicz
Khalid Ashraf
W. Dally
Kurt Keutzer
163
7,501
0
24 Feb 2016
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
ObjD
416
11,242
0
18 Jun 2014

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