Multigrain-aware Semantic Prototype Scanning and Tri-Token Prompt Learning Embraced High-Order RWKV for Pan-Sharpening

arXiv cs.CV / 4/17/2026

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Key Points

The paper introduces a multigrain-aware semantic prototype scanning approach for pan-sharpening to reduce RWKV’s positional bias by using semantic grouping (via locality-sensitive hashing) and context-aware token reordering.
It proposes tri-token prompt learning for RWKV, combining a global token, cluster-derived prototype tokens, and a learnable register token to improve semantic priors while suppressing noisy intermediate representations.
To better preserve spatial detail, the method adds an invertible multi-scale Q-shift feature transformation and uses center-difference convolution to inject high-frequency information without heavy parameter growth.
Experiments reported in the abstract indicate the proposed approach outperforms existing methods for pan-sharpening.

Abstract

In this work, we propose a Multigrain-aware Semantic Prototype Scanning paradigm for pan-sharpening, built upon a high-order RWKV architecture and a tri-token prompting mechanism derived from semantic clustering. Specifically, our method contains three key components: 1) Multigrain-aware Semantic Prototype Scanning. Although RWKV offers a efficient linear-complexity alternative to Transformers, its conventional bidirectional raster scanning is still semantic-agnostic and prone to positional bias. To address this issue, we introduce a semantic-driven scanning strategy that leverages locality-sensitive hashing to group semantically related regions and construct multi-grain semantic prototypes, enabling context-aware token reordering and more coherent global interaction. 2) Tri-token Prompt Learning. We design a tri-token prompting mechanism consisting of a global token, cluster-derived prototype tokens, and a learnable register token. The global and prototype tokens provide complementary semantic priors for RWKV modeling, while the register token helps suppress noisy and artifact-prone intermediate representations. 3) Invertible Q-Shift. To counteract spatial details, we apply center difference convolution on the value pathway to inject high-frequency information, and introduce an invertible multi-scale Q-shift operation for efficient and lossless feature transformation without parameter-heavy receptive field expansion. Experimental results demonstrate the superiority of our method.