Image coding for machines (ICM) aims to compress images to support downstream
AI analysis instead of human perception. For ICM, developing a unified codec to
reduce information redundancy while empowering the compressed features to
support various vision tasks is very important, which inevitably faces two core
challenges: 1) How should the compression strategy be adjusted based on the
downstream tasks? 2) How to well adapt the compressed features to different
downstream tasks? Inspired by recent advances in transferring large-scale
pre-trained models to downstream tasks via prompting, in this work, we explore
a new ICM framework, termed Prompt-ICM. To address both challenges by carefully
learning task-driven prompts to coordinate well the compression process and
downstream analysis. Specifically, our method is composed of two core designs:
a) compression prompts, which are implemented as importance maps predicted by
an information selector, and used to achieve different content-weighted bit
allocations during compression according to different downstream tasks; b)
task-adaptive prompts, which are instantiated as a few learnable parameters
specifically for tuning compressed features for the specific intelligent task.
Extensive experiments demonstrate that with a single feature codec and a few
extra parameters, our proposed framework could efficiently support different
kinds of intelligent tasks with much higher coding efficiency.

本研究提出了一个新的图像编码框架 (Prompt-ICM)，通过学习任务驱动提示来协调压缩过程和下游分析，解决了压缩策略的调整和压缩特征的任务适应性两个核心挑战，进而以高压缩效率支持不同的智能任务。