We present a simple and effective architecture for fine-grained visual
recognition called Bilinear Convolutional Neural Networks (B-CNNs). These
networks represent an image as a pooled outer product of features derived from
two CNNs and capture localized feature interactions in a translationally
invariant manner. B-CNNs belong to the class of orderless texture
representations but unlike prior work they can be trained in an end-to-end
manner. Our most accurate model obtains 84.1%, 79.4%, 86.9% and 91.3% per-image
accuracy on the Caltech-UCSD birds [67], NABirds [64], FGVC aircraft [42], and
Stanford cars [33] dataset respectively and runs at 30 frames-per-second on a
NVIDIA Titan X GPU. We then present a systematic analysis of these networks and
show that (1) the bilinear features are highly redundant and can be reduced by
an order of magnitude in size without significant loss in accuracy, (2) are
also effective for other image classification tasks such as texture and scene
recognition, and (3) can be trained from scratch on the ImageNet dataset
offering consistent improvements over the baseline architecture. Finally, we
present visualizations of these models on various datasets using top
activations of neural units and gradient-based inversion techniques. The source
code for the complete system is available at this http URL

该论文提出了一种称为 Bilinear 卷积神经网络（B-CNNs）的神经网络模型，通过使用两个卷积神经网络所提取的特征的外积计算来表示图像，并以平移不变的方式捕捉局部特征交互，并在多项图像分类数据集上展示了其在识别精度和速度等方面的优越性。