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[2602.12696] Channel-Aware Probing for Multi-Channel Imaging
Summary
The paper presents Channel-Aware Probing (CAP), a method for improving multi-channel imaging (MCI) performance by leveraging inter-channel diversity, enhancing probing with fixed representations, and matching fine-tuning results.
Why It Matters
This research addresses a significant gap in multi-channel imaging by proposing a novel probing technique that enhances the performance of vision encoders without the need for extensive fine-tuning. It offers a solution to the challenges posed by varying channel configurations across datasets, making it relevant for researchers and practitioners in computer vision and machine learning.
Key Takeaways
- Channel-Aware Probing (CAP) improves performance in multi-channel imaging tasks.
- The method effectively utilizes independent feature encoding and decoupled pooling.
- CAP reduces the performance gap compared to full fine-tuning approaches.
- The proposed technique is validated across three MCI benchmarks.
- This research opens avenues for better leveraging pre-trained encoders in MCI.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.12696 (cs) [Submitted on 13 Feb 2026] Title:Channel-Aware Probing for Multi-Channel Imaging Authors:Umar Marikkar, Syed Sameed Husain, Muhammad Awais, Sara Atito View a PDF of the paper titled Channel-Aware Probing for Multi-Channel Imaging, by Umar Marikkar and 3 other authors View PDF HTML (experimental) Abstract:Training and evaluating vision encoders on Multi-Channel Imaging (MCI) data remains challenging as channel configurations vary across datasets, preventing fixed-channel training and limiting reuse of pre-trained encoders on new channel settings. Prior work trains MCI encoders but typically evaluates them via full fine-tuning, leaving probing with frozen pre-trained encoders comparatively underexplored. Existing studies that perform probing largely focus on improving representations, rather than how to best leverage fixed representations for downstream tasks. Although the latter problem has been studied in other domains, directly transferring those strategies to MCI yields weak results, even worse than training from scratch. We therefore propose Channel-Aware Probing (CAP), which exploits the intrinsic inter-channel diversity in MCI datasets by controlling feature flow at both the encoder and probe levels. CAP uses Independent Feature Encoding (IFE) to encode each channel separately, and Decoupled Pooling (DCP) to pool within channels before aggregating across channels. Across three MCI benchma...
