iNAS: Integral NAS for Device-Aware Salient Object Detection

Introduction

Existing salient object detection (SOD) models usually focus on either backbone feature extractors or saliency heads, ignoring their relations. A powerful backbone could still achieve sub-optimal performance with a weak saliency head and vice versa. Moreover, the balance between model performance and inference latency poses a great challenge to model design, especially when considering different deployment scenarios. Considering all components in an integral neural architecture search (iNAS) space, we propose a flexible device-aware search scheme that only trains the SOD model once and quickly finds high-performance but low-latency models on multiple devices. An evolution search with latency-group sampling (LGS) is proposed to explore the entire latency area of our enlarged search space. Models searched by iNAS achieve similar performance with SOTA methods but reduce the 3.8×, 3.3×, 2.6×, 1.9× latency on Huawei Nova6 SE, Intel Core CPU, the Jetson Nano, and Nvidia Titan Xp.

Codes

Source Code and pre-trained model: https://github.com/guyuchao/iNAS .

Citation

@inproceedings{gu2021inas,
  title={iNAS: Integral NAS for Device-Aware Salient Object Detection},
  author={Gu, Yu-Chao and Gao, Shang-Hua and Cao, Xu-Sheng and Du, Peng and Lu, Shao-Ping and Cheng, Ming-Ming},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4934--4944},
  year={2021}
}