Attention Awareness Multiple Instance Neural Network

• URL: http://arxiv.org/abs/2205.13750v1
• Date: Fri, 27 May 2022 03:29:17 GMT
• Title: Attention Awareness Multiple Instance Neural Network
• Authors: Jingjun Yi and Beichen Zhou
• Abstract summary: We propose an attention awareness multiple instance neural network framework. 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.
• Score: 4.061135251278187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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