This paper presents a robust bag encoding strategy based on an MLP mixer. The proposed approach introduces the mixing concept to MIL applications, which helps to generate robust bag encoding. The existing bag encoding strategies for MIL applications consider instances in the bag as independent. This assumption may restrict the performance of these algorithms. Therefore, this paper proposes MIL-Mixer, which utilizes the information between the instances to generate a robust bag encoding. We also extend MLP-Mixture to use classification token similar to vision transformers which diversify the encoding generation process. In this study, three benchmark MIL datasets are used to assess the performance of the proposed MIL-Mixer. In comparison with existing MIL approaches, the proposed MIL-Mixer achieves better performance.

The Contribution to the 9th European Workshop on Visual Information Processing. The work is focuses on instance relevance estimation process in domain of multiple instance learning.