A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation
<|reference_start|>A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation: 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 frame such a challenging task as multi-objective optimization. To bridge the gap, we introduce a tailored streamline to transform the task of HW-NAS for real-time semantic segmentation into standard MOPs. Building upon the streamline, we present a benchmark test suite, CitySeg/MOP, comprising fifteen MOPs derived from the Cityscapes dataset. The CitySeg/MOP test suite is integrated into the EvoXBench platform to provide seamless interfaces with various programming languages (e.g., Python and MATLAB) for instant fitness evaluations. We comprehensively assessed the CitySeg/MOP test suite on various multi-objective evolutionary algorithms, showcasing its versatility and practicality. Source codes are available at https://github.com/EMI-Group/evoxbench.<|reference_end|>
@article{zhao2024a,
    title={A Multi-objective Optimization Benchmark Test Suite for Real-time
  Semantic Segmentation},
    author={Yifan Zhao, Zhenyu Liang, Zhichao Lu, Ran Cheng},
    journal={arXiv preprint arXiv:2404.16266},
    year={2024},
    doi={10.1145/3638530.3654389},
    archivePrefix={arXiv},
    eprint={2404.16266},
    primaryClass={cs.CV cs.NE}
}