arXiv Computer Vision August 27, 2026 By Haobo Xiong, Shaobo Liu, Kai Liu, Chongyang Ding

CrossMambaTuning: Synergistic Spatial and Cross-Layer Adaptation for Machine Vision Compression

Read the original on arXiv Computer Vision →

CrossMambaTuning is a new framework that adapts pretrained learned image compression models to machine vision tasks with minimal retraining. It combines State Space Models with cross‑layer interaction, featuring a Mamba adapter that uses task‑specific prompts and multi‑scale branching, and a Scale‑Invariant Cross‑Layer Adapter (SICA) that shares parameters across scales to reduce redundancy. Experiments show that this approach achieves state‑of‑the‑art performance while cutting parameter overhead by 72% compared to existing methods.

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