New architecture fuses RGB and thermal data for improved semantic segmentation

Researchers have developed a novel architecture for RGB-Thermal semantic segmentation, addressing challenges in adverse lighting conditions. The proposed method utilizes dual ConvNeXt V2 backbones with stage-wise, modality-adaptive fusion. It incorporates a Frequency-Based Fusion Module for early-stage features and a semantic fusion module with cross-modal attention for late-stage features, improving scene understanding by effectively integrating visible and infrared imagery. AI

IMPACT This research could lead to more robust computer vision systems for autonomous driving and other applications requiring scene understanding in challenging lighting conditions.

RANK_REASON The cluster contains a research paper detailing a new method for RGB-Thermal semantic segmentation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• ConvNeXt V2
• Ozgur Erkent
• PST900
• RGB-Thermal semantic segmentation

• paper
• model release

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