# TransTIC: Transferring Transformer-based Image Compression from Human Perception to Machine Perception

arXiv: https://arxiv.org/abs/2306.05085 · HTML: https://mlsanity.com/paper/2306.05085/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2023.

## Built on by (most cited first, 30 of 32)

- [Image Compression for Machine and Human Vision with Spatial-Frequency Adaptation](https://mlsanity.com/paper/2407.09853/) (2024)
- [All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation](https://mlsanity.com/paper/2409.19660/) (2024)
- [Generative Visual Compression: A Review](https://mlsanity.com/paper/2402.02140/) (2024)
- [Rate-Distortion-Cognition Controllable Versatile Neural Image Compression](https://mlsanity.com/paper/2407.11700/) (2024)
- [DT-UFC: Universal Large Model Feature Coding via Peaky-to-Balanced Distribution Transformation](https://mlsanity.com/paper/2506.16495/) (2025)
- [An Efficient Adaptive Compression Method for Human Perception and Machine Vision Tasks](https://mlsanity.com/paper/2501.04329/) (2025)
- [Unified Coding for Both Human Perception and Generalized Machine Analytics with CLIP Supervision](https://mlsanity.com/paper/2501.04579/) (2025)
- [Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Prior](https://mlsanity.com/paper/2511.22549/) (2025)
- [Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression](https://mlsanity.com/paper/2311.13846/) (2023)
- [Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines](https://mlsanity.com/paper/2503.19278/) (2025)
- [Explicit Residual-Based Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2506.19297/) (2025)
- [Revisiting MLLM Token Technology through the Lens of Classical Visual Coding](https://mlsanity.com/paper/2508.13460/) (2025)
- [When MLLMs Meet Compression Distortion: A Coding Paradigm Tailored to MLLMs](https://mlsanity.com/paper/2509.24258/) (2025)
- [SLIM: Semantic-based Low-bitrate Image compression for Machines by leveraging diffusion](https://mlsanity.com/paper/2512.18200/) (2025)
- [DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines](https://mlsanity.com/paper/2411.09308/) (2024)
- [A Perspective on Deep Vision Performance with Standard Image and Video Codecs](https://mlsanity.com/paper/2404.12330/) (2024)
- [All-in-One Transferring Image Compression from Human Perception to Multi-Machine Perception](https://mlsanity.com/paper/2504.12997/) (2025)
- [Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference](https://mlsanity.com/paper/2507.01608/) (2025)
- [Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Framework](https://mlsanity.com/paper/2511.08915/) (2025)
- [Progressive Learned Image Compression for Machine Perception](https://mlsanity.com/paper/2512.20070/) (2025)
- [End-to-End Semantic Preservation in Text-Aware Image Compression Systems](https://mlsanity.com/paper/2503.19495/) (2025)
- [Comp-X: On Defining an Interactive Learned Image Compression Paradigm With Expert-driven LLM Agent](https://mlsanity.com/paper/2508.15243/) (2025)
- [Codebook-Based Adaptive Feature Compression With Semantic Enhancement for Edge-Cloud Systems](https://mlsanity.com/paper/2509.18481/) (2025)
- [Benchmarking and Enhancing VLM for Compressed Image Understanding](https://mlsanity.com/paper/2512.20901/) (2025)
- [ARCHE: Autoregressive Residual Compression with Hyperprior and Excitation](https://mlsanity.com/paper/2603.10188/) (2026)
- [CI-ICM: Channel Importance-driven Learned Image Coding for Machines](https://mlsanity.com/paper/2604.05347/) (2026)
- [Multi-task Just Recognizable Difference for Video Coding for Machines: Database, Model, and Coding Application](https://mlsanity.com/paper/2604.09421/) (2026)
- [What and Where to Adapt: Structure-Semantics Co-Tuning for Machine Vision Compression via Synergistic Adapters](https://mlsanity.com/paper/2604.10017/) (2026)
- [Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification](https://mlsanity.com/paper/2605.08266/) (2026)
- [MoECodec: Image Compression for joint human and machine perception via Mixture-of-Experts](https://mlsanity.com/paper/2606.21033/) (2026)

## Abstract (arXiv)

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.