mlsanityData to 7 Oct 2026

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

arXiv 2306.05085 ↗First posted 8 Jun 2023eess.IVcited by 31

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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.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 17; 2026: 7.

31 papers.

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  1. 2024Image Compression for Machine and Human Vision with Spatial-Frequency Adaptationcited by 26
  2. 2024All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregationcited by 14
  3. 2024Generative Visual Compression: A Reviewcited by 13
  4. 2024Rate-Distortion-Cognition Controllable Versatile Neural Image Compressioncited by 11
  5. 2025DT-UFC: Universal Large Model Feature Coding via Peaky-to-Balanced Distribution Transformationcited by 6
  6. 2025An Efficient Adaptive Compression Method for Human Perception and Machine Vision Taskscited by 5
  7. 2025Unified Coding for Both Human Perception and Generalized Machine Analytics with CLIP Supervisioncited by 4
  8. 2025Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Priorcited by 4
  9. 2023Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compressioncited by 3
  10. 2025Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machinescited by 3
  11. 2025Explicit Residual-Based Scalable Image Coding for Humans and Machinescited by 3
  12. 2025Revisiting MLLM Token Technology through the Lens of Classical Visual Codingcited by 3
19 more
  1. 2025When MLLMs Meet Compression Distortion: A Coding Paradigm Tailored to MLLMscited by 3
  2. 2024DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machinescited by 2
  3. 2025SLIM: Semantic-based Low-bitrate Image compression for Machines by leveraging diffusioncited by 2
  4. 2024A Perspective on Deep Vision Performance with Standard Image and Video Codecscited by 1
  5. 2025All-in-One Transferring Image Compression from Human Perception to Multi-Machine Perceptioncited by 1
  6. 2025Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Frameworkcited by 1
  7. 2025Progressive Learned Image Compression for Machine Perceptioncited by 1
  8. 2025End-to-End Semantic Preservation in Text-Aware Image Compression Systemscited by 0
  9. 2025Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inferencecited by 0
  10. 2025Comp-X: On Defining an Interactive Learned Image Compression Paradigm With Expert-driven LLM Agentcited by 0
  11. 2025Codebook-Based Adaptive Feature Compression With Semantic Enhancement for Edge-Cloud Systemscited by 0
  12. 2025Benchmarking and Enhancing VLM for Compressed Image Understandingcited by 0
  13. 2026ARCHE: Autoregressive Residual Compression with Hyperprior and Excitationcited by 0
  14. 2026CI-ICM: Channel Importance-driven Learned Image Coding for Machinescited by 0
  15. 2026Multi-task Just Recognizable Difference for Video Coding for Machines: Database, Model, and Coding Applicationcited by 0
  16. 2026What and Where to Adapt: Structure-Semantics Co-Tuning for Machine Vision Compression via Synergistic Adapterscited by 0
  17. 2026Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classificationcited by 0
  18. 2026MoECodec: Image Compression for joint human and machine perception via Mixture-of-Expertscited by 0
  19. 2026CrossMambaTuning: Synergistic Spatial and Cross-Layer Adaptation for Machine Vision Compressioncited by 0