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Submission data

Full nameDeep Multimodal Networks with Residual Fusion Blocks
DescriptionConventional RGB-D semantic segmentation methods adopt two-stream fusion structure which uses two modality-specific encoders to extract features from the RGB and depth data. However, they do not fully exploit the interdependencies of the encoders. We proposes a novel bottom-up interactive fusion structure and residual fusion block to formulate the interdependencies of the two encoders.
Input Data TypesUses Color,Uses Geometry        Uses 2D
Programming language(s)Tensorflow
Submission creation date3 Jun, 2019
Last edited4 Jul, 2019

2D semantic label results

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