# Feature Coding in the Era of Large Models: Dataset, Test Conditions, and Benchmark

arXiv: https://arxiv.org/abs/2412.04307 · HTML: https://mlsanity.com/paper/2412.04307/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2024.

## Built on by (most cited first, 12 of 12)

- [DT-UFC: Universal Large Model Feature Coding via Peaky-to-Balanced Distribution Transformation](https://mlsanity.com/paper/2506.16495/) (2025)
- [Compressed Feature Quality Assessment: Dataset and Baselines](https://mlsanity.com/paper/2506.07412/) (2025)
- [Revisiting MLLM Token Technology through the Lens of Classical Visual Coding](https://mlsanity.com/paper/2508.13460/) (2025)
- [Just Noticeable Difference Modeling for Deep Visual Features](https://mlsanity.com/paper/2601.21933/) (2026)
- [Cross-architecture universal feature coding via distribution alignment](https://mlsanity.com/paper/2506.12737/) (2025)
- [DeDelayed: Deleting Remote Inference Delay via On-Device Correction](https://mlsanity.com/paper/2510.13714/) (2025)
- [Compression Tells Intelligence: Visual Coding, Visual Token Technology, and the Unification](https://mlsanity.com/paper/2601.20742/) (2026)
- [Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Framework](https://mlsanity.com/paper/2511.08915/) (2025)
- [Towards Large Model Feature Coding](https://mlsanity.com/paper/2605.24025/) (2026)
- [SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction](https://mlsanity.com/paper/2606.03940/) (2026)
- [Visual Token Codec: Unleashing Spatial Redundancy for ViT Feature Coding](https://mlsanity.com/paper/2608.08832/) (2026)
- [Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models](https://mlsanity.com/paper/2608.21247/) (2026)

## Abstract (arXiv)

Large models have achieved remarkable performance across various tasks, yet they incur significant computational costs and privacy concerns during both training and inference. Distributed deployment has emerged as a potential solution, but it necessitates the exchange of intermediate information between model segments, with feature representations serving as crucial information carriers. To optimize information exchange, feature coding is required to reduce transmission and storage overhead. Despite its importance, feature coding for large models remains an under-explored area. In this paper, we draw attention to large model feature coding and make three fundamental contributions. First, we introduce a comprehensive dataset encompassing diverse features generated by three representative types of large models. Second, we establish unified test conditions, enabling standardized evaluation pipelines and fair comparisons across future feature coding studies. Third, we introduce two baseline methods derived from widely used image coding techniques and benchmark their performance on the proposed dataset. These contributions aim to provide a foundation for future research and inspire broader engagement in this field. To support a long-term study, all source code and the dataset are made available at hrefhttps://github.com/chansongoal/LaMoFChttps://github.com/chansongoal/LaMoFC.