Parallel Task-Prompts ICM: A Versatile Feature Codec for Machine Vision

Abstract

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Image Coding for Machines (ICM) is developed to compress images with a focus on machine vision tasks rather than human perception. For ICM, It is very important to develop a universal codec adaptable to different machine tasks. In this paper, we propose novel parallel task-prompts that can be easily adapted to various machine vision tasks without necessitating new networks or scratch training. Besides, Our parallel prompts are compatible with mainstream backbones such as transformers and convolutional neural networks, making them widely applicable across different model architectures. In order to fine-tune our task-prompts, we leverage a machine task network as the teacher net, guiding our student ICM network to efficiently compress feature maps for downstream machine tasks. Through extensive experimentation on object detection and segmentation, we demonstrate that our proposed method surpasses traditional image compression techniques and state-of-the-art learning-based feature compression techniques in terms of rate-accuracy performance.