Transcription of Spatial Pyramid Pooling in Deep Convolutional Networks for ...
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Spatial Pyramid Pooling in Deep Convolutional Networks for ...
1 Spatial Pyramid Pooling in Deep ConvolutionalNetworks for Visual RecognitionKaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian SunAbstract Existing deep Convolutional neural Networks (CNNs) require a fixed-size ( , 224 224) input image. This require-ment is artificial and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In thiswork, we equip the Networks with another Pooling strategy, Spatial Pyramid Pooling , to eliminate the above requirement. Thenew network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramidpooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based imageclassification methods.
Spatial pyramid pooling [14], [15] (popularly known as spatial pyramid matching or SPM [15]), as an extension of the Bag-of-Words (BoW) model [16], is one of the most successful methods in computer vision. It partitions the image into divisions from finer to coarser levels, and aggregates local features in them. SPP has long been a key ...
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