A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation

arXiv:2404.16266v1 Announce Type: new
Abstract: As one of the emerging challenges in Automated Machine Learning, the Hardware-aware Neural Architecture Search (HW-NAS) tasks can be treated as black-box multi-objective optimization problems (MOPs). An important application of HW-NAS is real-time semantic segmentation, which plays a pivotal role in autonomous driving scenarios. The HW-NAS for real-time semantic segmentation inherently needs to balance multiple optimization objectives, including model accuracy, inference speed, and hardware-specific considerations. Despite its importance, benchmarks have yet to be developed to …

arxiv benchmark cs.cv cs.ne multi-objective optimization real-time segmentation semantic test type