SPATIAL PYRAMID POOLING NETWORKS FOR IMAGE PROCESSING

摘要

Spatial pyramid pooling (SPP) layers are combined with convolutional layers and partition an input image into divisions from finer to coarser levels, and aggregate local features in the divisions. A fixed-length output may be generated by the SPP layer(s) regardless of the input size. The multi-level spatial bins used by the SPP layer(s) may provide robustness to object deformations. An SPP layer based system may pool features extracted at variable scales due to the flexibility of input scales making it possible to generate a full-image representation for testing. Moreover, SPP networks may enable feeding of images with varying sizes or scales during training, which may increase scale-invariance and reduce the risk of over-fitting.

主权项

1. A method to perform image processing, the method comprising:
receiving an input image; generating feature maps by one or more filters on one or more convolutional layers of a neural network; spatially pooling responses of each filter of a top convolutional layer at a spatial pyramid pooling (SPP) network following the top convolutional layer, wherein the SPP network comprises one or more layers; and providing outputs of a top SPP network layer to a fully-connected layer as fixed dimensional vectors.