Cross-validated smooth multi-instance learning
Dayuan Li, Lin Zhu, Wenzheng Bao, Fei Cheng, Yi Cheng Ren, De-Shuang Huang · 2017
The problem of object localization in image appear ubiquitously in computer vision applications including image classification, object detection and visual tracking. Recently, it is shown that multiple-instance learning (MIL) which is regarded as the fourth machine learning framework compared with supervised learning, unsupervised learning and reinforce learning has been verified that will get good effect in object localization in images. In this paper, we propose a novel method to solve the classical MIL problem, named Cross-Validated Smooth Multi-Instance learning (CVS-MIL). We treat the positiveness of instance as a continuous variable. The softmax model is used to bring a bridge between instances and bags and jointly optimize the bag label and instance label in a unified framework. The extensive experiments demonstrate that CVS-MIL consistently achieves superior performance on various MIL benchmarks. Moreover, we simply applied CVS-MIL to a challenging vision task, common object discovery. The state-of-the-art results of object discovery on Pascal VOC datasets further confirm the advantages of the proposed method.