All-in-One Transferring Image Compression from Human Perception to Multi-Machine Perception

• URL: http://arxiv.org/abs/2504.12997v2
• Date: Mon, 04 Aug 2025 12:14:27 GMT
• Title: All-in-One Transferring Image Compression from Human Perception to Multi-Machine Perception
• Authors: Jiancheng Zhao, Xiang Ji, Yinqiang Zheng,
• Abstract summary: Existing approaches typically adapt LIC to downstream tasks in a single-task manner, which is inefficient, lacks task interaction, and results in multiple task-specific bitstreams.<n>We propose a multi-task adaptation framework that enables transferring a pre-trained base to multiple machine vision tasks through a unified model and a single training process.
• Score: 33.05779778716361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficiently transferring Learned Image Compression (LIC) model from human perception to machine perception is an emerging challenge in vision-centric representation learning. Existing approaches typically adapt LIC to downstream tasks in a single-task manner, which is inefficient, lacks task interaction, and results in multiple task-specific bitstreams. In this paper, we propose a multi-task adaptation framework that enables transferring a pre-trained base codec to multiple machine vision tasks through a unified model and a single training process. To achieve this, we design an asymmetric adaptation architecture consisting of a task-agnostic encoder adaptation and task-specific decoder adaptation. Furthermore, we introduce two feature propagation modules to facilitate inter-task and inter-scale feature represenation learning. Experiments on PASCAL-Context and NYUD-V2 dataset demonstrate that our method outperforms both Fully Fine-Tuned and other Parameter Efficient Fine-Tuned (PEFT) baselines. Code will be released.

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