mlsanityData to 14 Oct 2026

Deep feature compression for collaborative object detection

arXiv 1802.03931 ↗First posted 12 Feb 2018cs.CVcited by 48

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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.

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

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Citing papers per year: 2021: 11; 2022: 10; 2023: 5; 2024: 11; 2025: 6; 2026: 5.

48 papers.

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  1. 2021Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challengescited by 115
  2. 2022BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computingcited by 28
  3. 2023Federated Learning for Computer Visioncited by 22
  4. 2021Collaborative Intelligence: Challenges and Opportunitiescited by 19
  5. 2021Lightweight compression of neural network feature tensors for collaborative intelligencecited by 17
  6. 2022SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systemscited by 14
  7. 2022Resource-Constrained Edge AI with Early Exit Predictioncited by 11
  8. 2021Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligencecited by 7
  9. 2022Cross Modal Compression: Towards Human-comprehensible Semantic Compressioncited by 7
  10. 2022I-SPLIT: Deep Network Interpretability for Split Computingcited by 7
  11. 2024DMOFC: Discrimination Metric-Optimized Feature Compressioncited by 7
  12. 2021Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computationcited by 6
36 more
  1. 2022Rate-Distortion in Image Coding for Machinescited by 6
  2. 2021CALTeC: Content-Adaptive Linear Tensor Completion for Collaborative Intelligencecited by 5
  3. 2021Deep Joint Source-Channel Coding for Multi-Task Networkcited by 5
  4. 2022A Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN Computingcited by 5
  5. 2023Base Layer Efficiency in Scalable Human-Machine Codingcited by 4
  6. 2025Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Priorcited by 4
  7. 2024On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performancecited by 3
  8. 2024Mutual Information Analysis in Multimodal Learning Systemscited by 3
  9. 2026Just Noticeable Difference Modeling for Deep Visual Featurescited by 3
  10. 2021SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequencescited by 2
  11. 2022Privacy-Preserving Feature Coding for Machinescited by 2
  12. 2023Are Visual Recognition Models Robust to Image Compression?cited by 2
  13. 2024MTL-Split: Multi-Task Learning for Edge Devices using Split Computingcited by 2
  14. 2025Cross-architecture universal feature coding via distribution alignmentcited by 2
  15. 2025DeDelayed: Deleting Remote Inference Delay via On-Device Correctioncited by 2
  16. 2026Caption-once, Frames-on-Demand: Visual-Need Routing for Budget-Aware Agentic Long Video Understandingcited by 2
  17. 2022License Plate Privacy in Collaborative Visual Analysis of Traffic Scenescited by 1
  18. 2024Resilience of Entropy Model in Distributed Neural Networkscited by 1
  19. 2024Texture-guided Coding for Deep Featurescited by 1
  20. 2024Rethinking Learned Image Compression: Context is All You Needcited by 1
  21. 2024Enhancing Split Computing and Early Exit Applications through Predefined Sparsitycited by 1
  22. 2025Image Coding for Machines via Feature-Preserving Rate-Distortion Optimizationcited by 1
  23. 2025Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compressioncited by 1
  24. 2025New VVC profiles targeting Feature Coding for Machinescited by 1
  25. 2026Compressing AI Traffic: Standardized Neural Network Coding of Visual-Token Representations in Split Vision-Language Inferencecited by 1
  26. 2021Latent-Space Inpainting for Packet Loss Concealment in Collaborative Object Detectioncited by 0
  27. 2021Analysis of Latent-Space Motion for Collaborative Intelligencecited by 0
  28. 2021DFTS2: Simulating Deep Feature Transmission Over Packet Loss Channelscited by 0
  29. 2022ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligencecited by 0
  30. 2023MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learningcited by 0
  31. 2023Efficient IoT Inference via Context-Awarenesscited by 0
  32. 2024Compressive Feature Selection for Remote Visual Multi-Task Inferencecited by 0
  33. 2024Inter-Feature-Map Differential Coding of Surveillance Videocited by 0
  34. 2024Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Modelscited by 0
  35. 2026Understanding Rate-Distortion Performance in Distributed Transformer Inferencecited by 0
  36. 2026Bandwidth-adaptive Cloud-Assisted 360-Degree 3D Perception for Autonomous Vehiclescited by 0