Dissecting RGB-D Learning for Improved Multi-modal Fusion
This work addresses the black-box nature of multi-modal fusion in RGB-D vision, providing insights for researchers in computer vision, though it appears incremental as it builds on existing fusion strategies.
The paper tackled the problem of understanding complementary and fusion mechanisms in RGB-D models by presenting an analytical framework and novel score to dissect the community, revealing findings like cross-modal feature discrepancies and hybrid cooperation rules, and introduced a fusion strategy that delivered significant enhancements across tasks and multi-modal data.
In the RGB-D vision community, extensive research has been focused on designing multi-modal learning strategies and fusion structures. However, the complementary and fusion mechanisms in RGB-D models remain a black box. In this paper, we present an analytical framework and a novel score to dissect the RGB-D vision community. Our approach involves measuring proposed semantic variance and feature similarity across modalities and levels, conducting visual and quantitative analyzes on multi-modal learning through comprehensive experiments. Specifically, we investigate the consistency and specialty of features across modalities, evolution rules within each modality, and the collaboration logic used when optimizing a RGB-D model. Our studies reveal/verify several important findings, such as the discrepancy in cross-modal features and the hybrid multi-modal cooperation rule, which highlights consistency and specialty simultaneously for complementary inference. We also showcase the versatility of the proposed RGB-D dissection method and introduce a straightforward fusion strategy based on our findings, which delivers significant enhancements across various tasks and even other multi-modal data.