  Image compression for machine and human vision (ICMH) has gained increasing
attention in recent years. Existing ICMH methods are limited by high training
and storage overheads due to heavy design of task-specific networks. To address
this issue, in this paper, we develop a novel lightweight adapter-based tuning
framework for ICMH, named Adapt-ICMH, that better balances task performance and
bitrates with reduced overheads. We propose a spatial-frequency modulation
adapter (SFMA) that simultaneously eliminates non-semantic redundancy with a
spatial modulation adapter, and enhances task-relevant frequency components and
suppresses task-irrelevant frequency components with a frequency modulation
adapter. The proposed adapter is plug-and-play and compatible with almost all
existing learned image compression models without compromising the performance
of pre-trained models. Experiments demonstrate that Adapt-ICMH consistently
outperforms existing ICMH frameworks on various machine vision tasks with fewer
fine-tuned parameters and reduced computational complexity. Code will be
released at https://github.com/qingshi9974/ECCV2024-AdpatICMH .
