  Collaborative intelligence (CI) involves dividing an artificial intelligence
(AI) model into two parts: front-end, to be deployed on an edge device, and
back-end, to be deployed in the cloud. The deep feature tensors produced by the
front-end are transmitted to the cloud through a communication channel, which
may be subject to packet loss. To address this issue, in this paper, we propose
a novel approach to enhance the resilience of the CI system in the presence of
packet loss through Unequal Loss Protection (ULP). The proposed ULP approach
involves a feature importance estimator, which estimates the importance of
feature packets produced by the front-end, and then selectively applies Forward
Error Correction (FEC) codes to protect important packets. Experimental results
demonstrate that the proposed approach can significantly improve the
reliability and robustness of the CI system in the presence of packet loss.

Image and Video Processing;Computer Vision and Pattern Recognition