Image Understanding with Reinforcement Learning: Auto-tuning Image Attributes and Model Parameters for Object Detection and Segmentation

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Image Understanding with Reinforcement Learning: Auto-tuning Image Attributes and Model Parameters for Object Detection and Segmentation
Title:
Image Understanding with Reinforcement Learning: Auto-tuning Image Attributes and Model Parameters for Object Detection and Segmentation
Other Titles:
IEEE Transactions on Circuits and Systems for Video Technology
Publication Date:
02 May 2022
Citation:
Fang, F., Xu, Q., Cheng, Y., Sun, Y., & Lim, J.-H. (2022). Image Understanding with Reinforcement Learning: Auto-tuning Image Attributes and Model Parameters for Object Detection and Segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. https://doi.org/10.1109/tcsvt.2022.3171781
Abstract:
Models for image semantics understanding, such as deep learning (DL) models and mathematical models, are often trained on specific dataset or configured with specific parameters. When deploying such models on new tasks in a different test environment, it requires considerable effort to re-train the model or extensive expertise to tune the parameters. In this paper, we propose a smart reinforcement learning (RL) agent that could learn to tune parameters automatically to enhance model performance. The learning process is formulated as a generic control task for parameter adjustment, and applied to two use scenarios: (1) image attributes tuning to improve object detection performance on fixed DL model, and (2) parameter tuning of the mathematical model (Level Set) for image segmentation. We design a novel dynamic threshold mechanism in a multi-branch RL agent to effectively tune parameters of image qualities (for object detection) and Level Set models (for object segmentation). We conduct experiments on Pascal-VOC testing set, MS COCO validation set and a proprietary dataset of industrial components, where we achieve substantial improvement on object detection accuracy. We also perform experiments on the automatic parameter tuning of Level Set models. Results show that our method facilitates considerable performance improvement on public datasets compared with baseline method.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Funding Scheme
Grant Reference no. : A18A2b0046
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
ISSN:
1558-2205
1051-8215
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