arXiv Computer Vision
Adjoint Inversion Reveals Holographic Superposition and Destructive Interference in CNN Classifiers
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Extending Decoupled Attention to Dense Prediction and Masked Training for Multi-Channel Images
The paper introduces an extension of the Decoupled Vision Transformer (DC‑ViT) to handle multi‑channel imaging (MCI) data, where each channel carries a distinct semantic signal. By tokenizing each channel separately and then decoupling intra‑channel from inter‑channel updates, the model preserves channel‑specific features. The authors further enable independent per‑channel masking by solving a linear assignment between retained patches, allowing masked training without restricting visible tokens. Experiments on fluorescence microscopy, imaging mass cytometry, and satellite imaging datasets demonstrate that this approach outperforms the strongest Multi‑Channel Vision Transformer baselines on both classification and dense‑prediction tasks.
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ConCA: Concentration-Aware Channel Attention for Fine-Grained Visual Recognition
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From Preimage Search To Source-Grounded Feature Inversion
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Learning Features, Not Layouts: Feature-Axis Attention as a Transfer Prior for Medical Imaging
The paper introduces FAD‑ViT, a vision transformer that decouples spatial and feature axes of global attention, making feature‑axis attention the primary operator. Experiments on 23 medical imaging tasks show that shifting global attention from spatial to feature leaves from‑scratch performance unchanged but significantly boosts transfer learning, achieving up to three times the gain with fewer parameters and lower compute. The study demonstrates that prioritizing feature‑axis attention is a simple, cost‑effective design choice for improving medical image transfer.
arXiv Computer Vision
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ChanSFormer: A Channel Agnostic Vision Transformer for Multi-Channel Cell Painting Images
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arXiv Computer Vision
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CMAMBADEPTH: Self-supervised Monocular Depth Estimation with Channel Mamba and Hybrid Attention
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