We propose using multi-layer multiple instance learning (MMIL) for image set classification and applying it to the task of cannabis website classification. We treat each image as a...
In online tracking, the tracker evolves to reflect variations in object appearance and surroundings. This updating process is formulated as a supervised learning problem, thus a ...
We present a new approach to multiple instance learning (MIL) that is particularly effective when the positive bags are sparse (i.e. contain few positive instances). Unlike other ...
In this paper, we develop an efficient logistic regression model for multiple instance learning that combines L1 and L2 regularisation techniques. An L1 regularised logistic regr...
Traditional approaches to Multiple-Instance Learning (MIL) operate under the assumption that the instances of a bag are generated independently, and therefore typically learn an in...