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CV-IV: Cross-Modality Interactions between RGB and Thermal Imaging
- The integration of RGB and thermal data through cross-modality interactions has gained significant interest in the computer vision community.
- RGB and thermal imaging capture different information about a scene due to their distinct underlying principles and characteristics.
- RGB imaging operates in the visible spectrum, capturing color data based on light reflection, while thermal imaging detects infrared radiation emitted by objects based on their temperature.
- The detectors and lenses used in RGB and thermal cameras differ significantly, affecting sensitivity, cost, and resolution.
- RGB images are colorful representations based on visible light, while thermal images are grayscale representations indicative of temperature.
- Fusion strategies like early, late, and mid-fusion, along with attention mechanisms and transformer networks, play key roles in cross-modality interaction.
- Challenges in RGB-thermal fusion include modality gap, information redundancy, misaligned data, and computational costs in complex fusion methods.
- Standardized datasets like KAIST, Teledyne FLIR, and evaluation metrics like mAP and mIoU are essential for benchmarking cross-modal interaction methods.
- Survey papers provide in-depth analyses of cross-modality fusion techniques, challenges, and future research directions in the RGB-thermal imaging domain.
- Ongoing research focuses on optimizing fusion architectures, feature matching, image registration, and leveraging advanced techniques like graph neural networks.
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