Update projects

docs/source/projects.rst

@@ -13,6 +13,25 @@ If your work is built on **sc2bench**, start `a "Show and tell" discussion at Gi
 Papers
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+A Multi-task Supervised Compression Model for Split Computing
+----
+* Author(s): Yoshitomo Matsubara, Matteo Mendula, Marco Levorato
+* Venue: WACV 2025
+* PDF: `Paper <https://arxiv.org/abs/2501.01420>`_
+* Code: `GitHub <https://github.com/yoshitomo-matsubara/ladon-multi-task-sc2>`_
+
+Split computing (≠ split learning) is a promising approach to deep learning models for resource-constrained
+edge computing systems, where weak sensor (mobile) devices are wirelessly connected to stronger edge servers through
+channels with limited communication capacity. State-of-theart work on split computing presents methods for single tasks
+such as image classification, object detection, or semantic segmentation. The application of existing methods to
+multitask problems degrades model accuracy and/or significantly increase runtime latency. In this study, we propose Ladon,
+the first multi-task-head supervised compression model for multi-task split computing. Experimental results show that
+the multi-task supervised compression model either outperformed or rivaled strong lightweight baseline models in terms
+of predictive performance for ILSVRC 2012, COCO 2017, and PASCAL VOC 2012 datasets while learning compressed
+representations at its early layers. Furthermore, our models reduced end-to-end latency (by up to 95.4%) and
+energy consumption of mobile devices (by up to 88.2%) in multi-task split computing scenarios.
+
+
 FrankenSplit: Efficient Neural Feature Compression With Shallow Variational Bottleneck Injection for Mobile Edge Computing
 ----
 * Author(s): Alireza Furutanpey, Philipp Raith, Schahram Dustdar