Cross-Modal Knowledge Distillation for Low-Resource Intelligent Surveillance Systems
Keywords:
cross-modal knowledge distillation, low-resource surveillance, intelligent systems, system architecture, fairness, policyAbstract
The proliferation of intelligent surveillance systems has been driven by advances in deep learning, yet their deployment in low-resource environments—characterized by limited labeled data, constrained computational budgets, and unreliable connectivity—remains a critical challenge. Cross-modal knowledge distillation (CMKD) offers a promising paradigm for transferring representational capabilities from a large, multi-modal teacher model to a compact student model that operates on a single or reduced set of modalities. This paper presents a systems-level analysis of CMKD for low-resource intelligent surveillance, emphasizing structural trade-offs in architecture design, deployment infrastructure, operational sustainability, robustness, fairness, and governance. We argue that effective adoption of CMKD requires holistic consideration of the entire socio-technical stack, from sensor fusion and network topology to policy frameworks that govern data sovereignty and algorithmic accountability. A conceptual framework is introduced that maps the distillation pipeline onto real-world surveillance ecosystems, highlighting points of vulnerability and leverage. We examine how the choice of teacher modality, distillation objective, and student architecture affects system reliability under domain shift, adversarial perturbations, and resource fluctuations. Cross-domain comparisons with related transfer learning techniques—such as domain adaptation and self-supervised pretraining—are drawn to situate CMKD within the broader landscape of efficient machine learning. Forward-looking perspectives address the need for modular system design, federated distillation across edge nodes, and regulatory mechanisms that ensure equitable performance across demographic groups. The paper concludes by outlining a research agenda that integrates technical innovation with institutional accountability, positioning CMKD as a cornerstone for equitable and resilient surveillance infrastructure in under-resourced settings.
References
1. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems, vol. 25, 2012.
2. J. Redmon and A. Farhadi, "YOLO9000: better, faster, stronger," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 7263–7271.
3. Z. Wang, J. Yang, and M. A. Alsheikh, "Edge intelligence for smart surveillance in resource-constrained environments," IEEE Internet of Things Journal, vol. 8, no. 13, pp. 10595–10607, 2021.
4. S. Han, H. Mao, and W. J. Dally, "Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding," in International Conference on Learning Representations, 2016.
5. T. Chen, I. Goodfellow, and J. Shlens, "Net2Net: accelerating learning via knowledge transfer," in International Conference on Learning Representations, 2016.
6. S. T. K. Nguyen, J. C. Y. Shin, and J. H. Park, "Cross-modal knowledge distillation for unsupervised thermal object detection," IEEE Access, vol. 8, pp. 222388–222400, 2020.
7. A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio, "FitNets: hints for thin deep nets," in International Conference on Learning Representations, 2015.
8. J. Wang, J. Tang, and J. Luo, "A survey of edge computing for intelligent surveillance systems," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 9, pp. 3835–3850, 2021.
9. S. Gupta, J. Hoffman, and J. Malik, "Cross modal distillation for supervision transfer," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2827–2836.
10. G. Hinton, O. Vinyals, and J. Dean, "Distilling the knowledge in a neural network," arXiv preprint arXiv:1503.02531, 2015.
11. S. Rezaei and M. Shah, "Cross-modal knowledge distillation for multi-modal action recognition," in European Conference on Computer Vision Workshops, 2018.
12. D. Kim, H. Lee, and Y. Kim, "Thermal object detection via cross-modal distillation from visible images," IEEE Transactions on Image Processing, vol. 30, pp. 5431–5444, 2021.
13. A. Owens and A. Efros, "Audio-visual scene analysis with self-supervised learning," in Advances in Neural Information Processing Systems, vol. 30, 2017.
14. G. Gallego, T. Delbruck, and G. Orchard, "Event-based vision: a survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 1, pp. 154–180, 2022.
15. D. Park, S. Kim, and T. Kim, "Cross-modal knowledge distillation for pedestrian detection in thermal images," in IEEE International Conference on Image Processing, 2020, pp. 2541–2545.
16. P. Panagiotakis and A. Argyros, "Skeleton-based action recognition via knowledge distillation from video models," Image and Vision Computing, vol. 110, art. 104182, 2021.
17. L. Zhang, X. Chen, and C. Li, "Low-light anomaly detection using cross-modal distillation," in IEEE Winter Conference on Applications of Computer Vision, 2022, pp. 1893–1902.
18. Y. Ganin and V. Lempitsky, "Unsupervised domain adaptation by backpropagation," in International Conference on Machine Learning, 2015, pp. 1180–1189.
19. J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, "How transferable are features in deep neural networks?" in Advances in Neural Information Processing Systems, vol. 27, 2014.
20. W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge computing: vision and challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016.
21. H. Li, K. Ota, and M. Dong, "Learning IoT in edge: deep learning for the Internet of Things with edge computing," IEEE Network, vol. 32, no. 1, pp. 96–101, 2018.
22. Z. Li, D. Hoiem, and D. Forsyth, "Continual learning for sensor-based surveillance in changing environments," ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 4, pp. 1–24, 2022.
23. Q. Yang, Y. Liu, T. Chen, and Y. Tong, "Federated machine learning: concept and applications," ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 1–19, 2019.
24. N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, "Distillation as a defense to adversarial perturbations against deep neural networks," in IEEE Symposium on Security and Privacy, 2016, pp. 582–597.
25. J. Buolamwini and T. Gebru, "Gender shades: intersectional accuracy disparities in commercial gender classification," in Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 2018, pp. 77–91.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Advanced Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
