Convolutional neural networks (CNN) have shown great performance as a global representation for object recognition. However, for multi-label images that contain multiple objects from different categories, scales and locations, single CNN features might not be be optimal. To enhance the robustness and discriminative power of CNN features for multi-label object recognition problem, we propose a multi-view multi-instance framework. This framework transforms the multi-label classification problem into a multi-class multi-instance learning problem by extracting object proposals from images. A multi-view pipeline is then applied to generate a two-view representation of each proposal by exploiting two levels of labels in multi-label recognition problem. The proposed framework not only has the flexibility of utilizing both weak and strong labels or just weak labels, but also holds the generalization ability to boost the performance of unseen categories by available strong labels. Our framework is extensively compared with state-of-the-art hand-crafted feature based and CNN based methods on two multi-label benchmark datasets. The experimental results validate the discriminative power and generalization ability of the proposed framework. When combined with a very-deep network, we can achieve state-of-the-art results in both datasets.

本文提出了一种基于卷积神经网络的多视图多实例框架，用于提高物体检测中的特征表达和泛化能力，通过利用局部信息、弱标签和强标签有效地识别包含多个物体的多标签图像，实验结果表明该框架具有优异的鉴别力和泛化能力。

利用边界框注释进行多标签物体识别