PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection

Going deeper with convolutions

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015

Earlier work this paper cites.

Batch normalization: Accelerating deep network training by reducing internal covariate shift

Sergey Ioffe and Christian Szegedy · 2015

Earlier work this paper cites.

Hypercolumns for object segmentation and fine-grained localization

Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2015

Earlier work this paper cites.

Faster R-CNN: Towards real-time object detection with region proposal networks

Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015

Earlier work this paper cites.

Fast algorithms for convolutional neural networks

Original

Andrew Lavin and Scott Gray · 2015

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Object detection via a multi-region & semantic segmentation-aware CNN model

Spyros Gidaris and Nikos Komodakis · 2015

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Fast R-CNN

Ross Girshick · 2015

Cited alongside, same era.

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016

Cited alongside, same era.

Understanding and improving convolutional neural networks via concatenated rectified linear units

Wenling Shang, Kihyuk Sohn, Diogo Almeida, and Honglak Lee · 2016

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HyperNet: Towards accurate region proposal generation and joint object detection

Tao Kong, Anbang Yao, Yurong Chen, and Fuchun Sun · 2016

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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks

Sean Bell, C. Lawrence Zitnick, Kavita Bala, and Ross Girshick · 2016

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R-fcn : Object detection via region-based fully convolutional networks

Jifeng Dai, Yi Li, Kaiming He, and Jian Sun · 2016

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