Bilinear CNN Models for Fine-grained Visual Recognition

Bilinear CNN Models for Fine-grained Visual RecognitionTsung-Yu LinAruni RoyChowdhurySubhransu MajiUniversity of Massachusetts, propose Bilinear Models , a Recognition architecturethat consists of two feature extractors whose outputs aremultiplied using outer product at each location of the im-age and pooled to obtain an image descriptor. This archi-tecture can model local pairwise feature interactions in atranslationally invariant manner which is particularly use-ful for Fine-grained categorization. It also generalizes var-ious orderless texture descriptors such as the Fisher vec-tor, VLAD and O2P. We present experiments with bilinearmodels where the feature extractors are based on convolu-tional neural networks. The Bilinear form simplifies gra-dient computation and allows end-to-end training of bothnetworks using image labels only. Using networks initial-ized from the ImageNet dataset followed by domain spe-cific fine -tuning we obtain accuracy of the CUB-200-2011 dataset requiring only category labels at train-ing time.

Fine-grained recognition tasks such as identifying the species of a bird, or the model of an aircraft, are quite challenging because the visual differences between the cat-egories are small and can be easily overwhelmed by those causedbyfactorssuchaspose,viewpoint,orlocationofthe object in the image. For example, the inter-category vari-

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  Fine, Recognition, Object

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