Multi-view Classification Using Hybrid Fusion and Mutual Distillation

Multi-view classification problems are common in medical image analysis, forensics, and other domains where problem queries involve multi-image input. Existing multi-view classification methods are often tailored to a specific task. In this paper, we repurpose off-the-shelf Hybrid CNN-Transformer networks for multi-view classification with either structured or unstructured views. Our approach incorporates a novel fusion scheme, mutual distillation, and minimal additional parameters. We demonstrate the effectiveness and generalization capability of our approach, MV-HFMD, on multiple multi-view classification tasks and show that it outperforms other multi-view approaches, even task-specific methods. Code is available at https://github.com/vidarlab/multi-view-hybrid.