@inproceedings{4370a1899a4740909226e70ce4726b13,
title = "TransTIC: Transferring Transformer-based Image Compression from Human Perception to Machine Perception",
abstract = "This work aims for transferring a Transformer-based image compression codec from human perception to machine perception without fine-tuning the codec. We propose a transferable Transformer-based image compression framework, termed TransTIC. Inspired by visual prompt tuning, TransTIC adopts an instance-specific prompt generator to inject instance-specific prompts to the encoder and task-specific prompts to the decoder. Extensive experiments show that our proposed method is capable of transferring the base codec to various machine tasks and outperforms the competing methods significantly. To our best knowledge, this work is the first attempt to utilize prompting on the low-level image compression task.",
author = "Chen, {Yi Hsin} and Weng, {Ying Chieh} and Kao, {Chia Hao} and Cheng Chien and Chiu, {Wei Chen} and Peng, {Wen Hsiao}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 ; Conference date: 02-10-2023 Through 06-10-2023",
year = "2023",
doi = "10.1109/ICCV51070.2023.02129",
language = "English",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "23240--23250",
booktitle = "Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023",
address = "United States",
}