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

Prompt-ICM: A Unified Framework towards Image Coding for Machines with Task-driven Prompts