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MMFormer: Multi-Modality semi-Supervised vision transformer in remote sensing imagery classification
Daixun Li1, Weiying Xie1, Leyuan Fang2
1State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, 710071, Shannxi, China.
Summary
MMFormer, a new semi-supervised algorithm, enhances multimodal fusion by leveraging feature complementarity and consistency. This approach improves accuracy on high-dimensional data, even with sparse annotations.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Transformer architectures are advancing multimodal tasks.
- Current fusion methods neglect feature complementarity and consistency, causing redundant fusion and incomplete representations.
Purpose of the Study:
- Introduce MMFormer, a novel semi-supervised algorithm for high-dimensional multimodal fusion.
- Enhance interactivity between modal mappings for comprehensive representations.
- Address challenges of sparse annotations in high-dimensional multimodal data.
Main Methods:
- Inspired by topological homology groups, MMFormer uses a complete dictionary lookup and homology space for representational consistency.
- Employs an exclusivity-aware mapping to highlight complementary modal information.
- Introduces a consistency joint regularization term to mitigate sparse annotation issues.
Main Results:
- MMFormer demonstrates superiority across three benchmarks (Houston2013, Augsburg, MUUFL).
- Achieved accuracy improvements of 3.12% (Houston2013), 1.86% (Augsburg), and 1.66% (MUUFL).
- Confirms robustness and effectiveness under sparse annotation conditions.
Conclusions:
- MMFormer offers a unified end-to-end optimization framework for high-dimensional multimodal fusion.
- Represents the first application of semi-supervised visual transformers in this domain.
- Effectively enhances multimodal feature interpretation and fusion accuracy.
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