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[2504.19223] CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis
Summary
The paper presents CARL, a camera-agnostic model for spectral image analysis that enhances AI methodologies across various imaging modalities, addressing limitations in cross-camera applicability.
Why It Matters
As spectral imaging becomes crucial in fields like medicine and autonomous driving, CARL's ability to generalize across different cameras enhances the development of robust AI models. This advancement could significantly improve applications in remote sensing and medical diagnostics, making it a pivotal contribution to the field.
Key Takeaways
- CARL introduces a novel spectral encoder for camera-agnostic representation.
- The model demonstrates superior performance in diverse applications, including medical imaging and autonomous driving.
- It addresses the challenge of spectral heterogeneity, enhancing model robustness.
- Spatio-spectral pre-training is achieved through a unique self-supervision strategy.
- The code and model weights are publicly available, promoting further research and development.
Computer Science > Computer Vision and Pattern Recognition arXiv:2504.19223 (cs) [Submitted on 27 Apr 2025 (v1), last revised 17 Feb 2026 (this version, v4)] Title:CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis Authors:Alexander Baumann, Leonardo Ayala, Silvia Seidlitz, Jan Sellner, Alexander Studier-Fischer, Berkin Özdemir, Lena Maier-Hein, Slobodan Ilic View a PDF of the paper titled CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis, by Alexander Baumann and 7 other authors View PDF HTML (experimental) Abstract:Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding, and is already established as a critical modality in remote sensing. However, variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies, leading to camera-specific models with limited generalizability and inadequate cross-camera applicability. To address this bottleneck, we introduce CARL, a model for Camera-Agnostic Representation Learning across RGB, multispectral, and hyperspectral imaging modalities. To enable the conversion of a spectral image with any channel dimensionality to a camera-agnostic representation, we introduce a novel spectral encoder, featuring a self-attention-cross-attention mechanism, to distill salient spectral information into learned spectral representations. Spatio-spectral pre-training is ac...
