论文标题
注意意识多个实例神经网络
Attention Awareness Multiple Instance Neural Network
论文作者
论文摘要
多个实例学习有资格用于具有弱注释数据的许多模式识别任务。人工神经网络和多个实例学习的结合提供了端到端的解决方案，并已广泛使用。但是，挑战仍然两倍。首先，当前的MIL合并操作员通常是预定的，并且缺乏对关键实例的灵活性。其次，在当前的解决方案中，行李级代表可能是不准确的或无法访问的。为此，我们在本文中提出了注意力意识多个实例神经网络框架。它由实例级分类器，一个基于空间注意的可训练的MIL合并操作员和一个行李级分类层组成。一系列模式识别任务的详尽实验表明，我们的框架的表现优于许多最先进的MIL方法，并验证了我们提出的注意MIL PORMING运营商的有效性。
Multiple instance learning is qualified for many pattern recognition tasks with weakly annotated data. The combination of artificial neural network and multiple instance learning offers an end-to-end solution and has been widely utilized. However, challenges remain in two-folds. Firstly, current MIL pooling operators are usually pre-defined and lack flexibility to mine key instances. Secondly, in current solutions, the bag-level representation can be inaccurate or inaccessible. To this end, we propose an attention awareness multiple instance neural network framework in this paper. It consists of an instance-level classifier, a trainable MIL pooling operator based on spatial attention and a bag-level classification layer. Exhaustive experiments on a series of pattern recognition tasks demonstrate that our framework outperforms many state-of-the-art MIL methods and validates the effectiveness of our proposed attention MIL pooling operators.
