Distributed Semantic Segmentation With Improved Rate-Distortion Trade-Off

Abstract:Distributed deep neural networks (DNNs) for dense perception tasks such as semantic segmentation execute an encoder DNN on edge devices, and a decoder DNN typically on a large-scale cloud platform with a particular constraint on transmission bitrate. Recent works employ source codecs to enable bitrate-efficient transmission between the edge device and the cloud. However, as these approaches are typically bound to a particular type of source codec and alternative network architectures are often not explored, this results in a suboptimal rate-distortion (RD) trade-off in the low-bitrate regime. In this work, we propose two novel source codecs that \textit{enable extremely low bitrates, while improving RD performance}. We demonstrate the effectiveness of our proposed source codecs by achieving state-of-the-art performance in distributed semantic segmentation at below 0.2 (0.03) bits per pixel, measured using the mean intersection-over-union metric on ADE20K (Cityscapes).

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2608.28684 [cs.CV]

(or arXiv:2608.28684v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2608.28684 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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