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

• URL: http://arxiv.org/abs/2504.12997v1
• Date: Thu, 17 Apr 2025 15:06:52 GMT
• Title: All-in-One Transferring Image Compression from Human Perception to Multi-Machine Perception
• Authors: Jiancheng Zhao, Xiang Ji, Zhuoxiao Li, Zunian Wan, Weihang Ran, Mingze Ma, Muyao Niu, Yifan Zhan, Cheng-Ching Tseng, Yinqiang Zheng,
• Abstract summary: We propose an asymmetric adaptor framework that supports multi-task adaptation within a single model.<n>Our method achieves strong performance across multiple tasks while maintaining compression efficiency.
• Score: 23.930457697876566
• 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. To address these limitations, we propose an asymmetric adaptor framework that supports multi-task adaptation within a single model. Our method introduces a shared adaptor to learn general semantic features and task-specific adaptors to preserve task-level distinctions. With only lightweight plug-in modules and a frozen base codec, our method achieves strong performance across multiple tasks while maintaining compression efficiency. Experiments on the PASCAL-Context benchmark demonstrate that our method outperforms both Fully Fine-Tuned and other Parameter Efficient Fine-Tuned (PEFT) baselines, and validating the effectiveness of multi-vision transferring.

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