Stefan Mathe and Cristian Sminchisescu. Multiple instance reinforcement learning for efficient weakly- supervised detection in images. arXiv preprint arXiv:1412.0100, 2014.S. Mathe and C. Sminchisescu. Multiple instance reinforce- ment learning for efficient weakly-supervised detection in images. ...
Multiple Instance Reinforcement Learning for Efficient Weakly-Supervised Detection in Images State-of-the-art visual recognition and detection systems increasingly rely on large amounts of training data and complex classifiers. Therefore it becomes... S Mathe,C Sminchisescu - 《Computer Science》 被...
In this work, we develop a new algorithm that combines the strengths of Kernel-Based Reinforcement Learning, which features instance-based state representation and kernel-based function approximation, and Prioritized Sweeping, which features model-based exploration. The resulting algorithm, Kernel-Based ...
The process of active object detection (active learning for object detection) is shown in the figure below. First, a small set of images (the labeled set) with instance labels and a large set of images (the unlabeled set) without labels are given. For each image, the label consists of ...
The conventional Robertson’s preparation uncertainty can be viewed as a specific instance of this refinement by consistently setting \({\tilde{S}}_{{u}_{q}}={S}_{{u}_{q}}\). This refined formulation thus also paves the way for tighter preparation uncertainty bounds with independent ...
For instance, hydrodynamic heat exchange models are traditionally used to determine environmental flows in rivers, particularly through methodologies such as the instream flow incremental method. However, these models often entail high costs due to their inherent limitations. Adapting the proposed ...
M.: Adaptive Reactive Job-Shop Scheduling with Learning Agents Traditional approaches to solving job-shop scheduling problems assume full knowledge of the problem and search for a centralized solution for a single problem instance. Finding optimal solutions, however, requires an enormous computation......
For instance, using functional magnetic resonance imaging (fMRI), dissociable components of the brain’s valuation network1 were found to track how much participants liked a set of choice options overall versus elements of the choice process itself, for example, whether they were engaged in choice...
For instance, using functional magnetic resonance imaging (fMRI), dissociable components of the brain’s valuation network1 were found to track how much participants liked a set of choice options overall versus elements of the choice process itself, for example, whether they were engaged in choice...
当MIL数据集在instance-level或bag-level不平衡时,学习到的margin 将受到majority class(通常为negative 类)的影响。 在IS范例中,由于训练了一个实例级分类器来区分positive bags 中的实例和negative bag中的实例,学习的margin 中的bias 可以用与单实例分类器的不平衡问题相同的方式来解释[11],因为true positive in...