  Multimodal semantic segmentation is a pivotal component of computer vision
and typically surpasses unimodal methods by utilizing rich information set from
various sources.Current models frequently adopt modality-specific frameworks
that inherently biases toward certain modalities. Although these biases might
be advantageous in specific situations, they generally limit the adaptability
of the models across different multimodal contexts, thereby potentially
impairing performance. To address this issue, we leverage the inherent
capabilities of the model itself to discover the optimal equilibrium in
multimodal fusion and introduce U3M: An Unbiased Multiscale Modal Fusion Model
for Multimodal Semantic Segmentation. Specifically, this method involves an
unbiased integration of multimodal visual data. Additionally, we employ feature
fusion at multiple scales to ensure the effective extraction and integration of
both global and local features. Experimental results demonstrate that our
approach achieves superior performance across multiple datasets, verifing its
efficacy in enhancing the robustness and versatility of semantic segmentation
in diverse settings. Our code is available at
U3M-multimodal-semantic-segmentation.
