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Segment scene with RGB-D images by efficiently fusing RGB and depth features with MIPANet

Segment scene with RGB-D images by efficiently fusing RGB and depth features with MIPANet


Optimizing rgb-d semantic segmentation through multi-modal interaction and pooling attention

arXiv paper abstract https://arxiv.org/abs/2311.11312



Semantic segmentation of RGB-D images involves understanding the appearance and spatial relationships of objects ... However ... RGB and depth images often results in ... suboptimal segmentation


... propose the Multi-modal Interaction and Pooling Attention Network (MIPANet) ... to harness the interactive synergy between RGB and depth modalities, optimizing the utilization of complementary information.


... incorporate a Multi-modal Interaction Fusion Module (MIM) into the deepest layers of the network.


This module is engineered to facilitate the fusion of RGB and depth information, allowing for mutual enhancement and correction.


... introduce a Pooling Attention Module (PAM) ... amplify the features extracted by the network and integrates the module's output into the decoder in a targeted manner ... improving semantic segmentation


... MIPANet outperforms existing methods on two indoor scene datasets ...



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