Enabling Next-Generation Consumer Experience with Feature Coding for Machines
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Abstract (arXiv)
As consumer devices become increasingly intelligent and interconnected, efficient data transfer solutions for machine tasks have become essential. This paper presents an overview of the latest Feature Coding for Machines (FCM) standard, part of MPEG-AI and developed by the Moving Picture Experts Group (MPEG). FCM supports AI-driven applications by enabling the efficient extraction, compression, and transmission of intermediate neural network features. By offloading computationally intensive operations to base servers with high computing resources, FCM allows low-powered devices to leverage large deep learning models. Experimental results indicate that the FCM standard maintains the same level of accuracy while reducing bitrate requirements by 75.90% compared to remote inference.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 16 references that are in the map. First posted on arXiv 10 Dec 2025.
Builds on
5 of 16 references.
References · 5
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- 2017Mask R-CNNcited by 7,259
- 2018The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalecited by 542
- 2019Large-scale interactive object segmentation with human annotatorscited by 109
- 2021Collaborative Intelligence: Challenges and Opportunitiescited by 18
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Citing papers per year: 2026: 1.
1 paper.
- 2026Towards Large Model Feature Codingcited by 1
- 2026Towards Large Model Feature Coding20 May 2026