Patch-level image representation is very important for object classification
and detection, since it is robust to spatial transformation, scale variation,
and cluttered background. Many existing methods usually require fine-grained
supervisions (e.g., bounding-box annotations) to learn patch features, which
requires a great effort to label images may limit their potential applications.
In this paper, we propose to learn patch features via weak supervisions, i.e.,
only image-level supervisions. To achieve this goal, we treat images as bags
and patches as instances to integrate the weakly supervised multiple instance
learning constraints into deep neural networks. Also, our method integrates the
traditional multiple stages of weakly supervised object classification and
discovery into a unified deep convolutional neural network and optimizes the
network in an end-to-end way. The network processes the two tasks object
classification and discovery jointly, and shares hierarchical deep features.
Through this jointly learning strategy, weakly supervised object classification
and discovery are beneficial to each other. We test the proposed method on the
challenging PASCAL VOC datasets. The results show that our method can obtain
state-of-the-art performance on object classification, and very competitive
results on object discovery, with faster testing speed than competitors.

本文提出了一种基于弱监督的学习方法，通过将图像视为一个包中的实例将其融入到深度神经网络中，将弱监督的多实例学习约束与深度神经网络集成在一起，并采用端到端的方式优化网络以实现对象分类和发现任务。在 PASCAL VOC 数据集上的测试表明，该方法在对象分类方面可以获得最先进的性能，并且在对象发现方面也具有很强的竞争力，并且测试速度比竞争者更快。