Compact Bilinear Pooling
Title | Compact Bilinear Pooling |
Publication Type | Conference Paper |
Year of Publication | 2016 |
Authors | Gao, Y., Beijbom O., Zhang N., & Darrell T. |
Published in | The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
Page(s) | 317-326 |
Date Published | 06/2016 |
Abstract | Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. However, bilinear features are high dimensional, typically on the order of hundreds of thousands to a few million, which makes them impractical for subsequent analysis. We propose two compact bilinear representations with the same discriminative power as the full bilinear representation but with only a few thousand dimensions. Our compact representations allow back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system. The compact bilinear representations are derived through a novel kernelized analysis of bilinear pooling which provide insights into the discriminative power of bilinear pooling, and a platform for further research in compact pooling methods. Experimentation illustrate the utility of the proposed representations for image classification and few-shot learning across several datasets. |
URL | http://www.icsi.berkeley.edu/pubs/vision/compactbilinearpooling16.pdf |
ICSI Research Group | Vision |