Compact Bilinear Pooling

Understand

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.