# Deep feature compression for collaborative object detection

arXiv: https://arxiv.org/abs/1802.03931 · HTML: https://mlsanity.com/paper/1802.03931/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2018.

## Built on by (most cited first, 30 of 48)

- [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021)
- [BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing](https://mlsanity.com/paper/2201.02693/) (2022)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Collaborative Intelligence: Challenges and Opportunities](https://mlsanity.com/paper/2102.06841/) (2021)
- [Lightweight compression of neural network feature tensors for collaborative intelligence](https://mlsanity.com/paper/2105.06002/) (2021)
- [SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems](https://mlsanity.com/paper/2204.04705/) (2022)
- [Resource-Constrained Edge AI with Early Exit Prediction](https://mlsanity.com/paper/2206.07269/) (2022)
- [Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence](https://mlsanity.com/paper/2105.07102/) (2021)
- [Cross Modal Compression: Towards Human-comprehensible Semantic Compression](https://mlsanity.com/paper/2209.02574/) (2022)
- [I-SPLIT: Deep Network Interpretability for Split Computing](https://mlsanity.com/paper/2209.11607/) (2022)
- [DMOFC: Discrimination Metric-Optimized Feature Compression](https://mlsanity.com/paper/2405.04044/) (2024)
- [Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computation](https://mlsanity.com/paper/2105.00591/) (2021)
- [Rate-Distortion in Image Coding for Machines](https://mlsanity.com/paper/2209.11694/) (2022)
- [CALTeC: Content-Adaptive Linear Tensor Completion for Collaborative Intelligence](https://mlsanity.com/paper/2106.05531/) (2021)
- [Deep Joint Source-Channel Coding for Multi-Task Network](https://mlsanity.com/paper/2109.05779/) (2021)
- [A Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN Computing](https://mlsanity.com/paper/2208.11596/) (2022)
- [Base Layer Efficiency in Scalable Human-Machine Coding](https://mlsanity.com/paper/2307.02430/) (2023)
- [Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Prior](https://mlsanity.com/paper/2511.22549/) (2025)
- [On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performance](https://mlsanity.com/paper/2403.17154/) (2024)
- [Mutual Information Analysis in Multimodal Learning Systems](https://mlsanity.com/paper/2405.12456/) (2024)
- [Just Noticeable Difference Modeling for Deep Visual Features](https://mlsanity.com/paper/2601.21933/) (2026)
- [SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences](https://mlsanity.com/paper/2112.14934/) (2021)
- [Privacy-Preserving Feature Coding for Machines](https://mlsanity.com/paper/2210.00727/) (2022)
- [Are Visual Recognition Models Robust to Image Compression?](https://mlsanity.com/paper/2304.04518/) (2023)
- [MTL-Split: Multi-Task Learning for Edge Devices using Split Computing](https://mlsanity.com/paper/2407.05982/) (2024)
- [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)
- [Caption-once, Frames-on-Demand: Visual-Need Routing for Budget-Aware Agentic Long Video Understanding](https://mlsanity.com/paper/2609.11899/) (2026)
- [License Plate Privacy in Collaborative Visual Analysis of Traffic Scenes](https://mlsanity.com/paper/2205.01724/) (2022)
- [Resilience of Entropy Model in Distributed Neural Networks](https://mlsanity.com/paper/2403.00942/) (2024)

## Abstract (arXiv)

Recent studies have shown that the efficiency of deep neural networks in mobile applications can be significantly improved by distributing the computational workload between the mobile device and the cloud. This paradigm, termed collaborative intelligence, involves communicating feature data between the mobile and the cloud. The efficiency of such approach can be further improved by lossy compression of feature data, which has not been examined to date. In this work we focus on collaborative object detection and study the impact of both near-lossless and lossy compression of feature data on its accuracy. We also propose a strategy for improving the accuracy under lossy feature compression. Experiments indicate that using this strategy, the communication overhead can be reduced by up to 70% without sacrificing accuracy.