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Global and Local Multi-scale Feature Fusion for Object Detection and Semantic Segmentation

Title
Global and Local Multi-scale Feature Fusion for Object Detection and Semantic Segmentation
Authors
Lim, Young ChulKang, Minsung
DGIST Authors
Lim, Young Chul; Kang, Minsung
Issue Date
2019-06-12
Citation
30th IEEE Intelligent Vehicles Symposium, IV 2019, 2557-2562
Type
Conference
ISBN
9781728105604
Abstract
Feature fusion approaches have been widely used in object detection and semantic segmentation to improve accuracy. Global feature fusion integrates semantic information and detail spatial information. Combining the fine feature maps in the bottom-up stage and the coarse feature maps in the top-down stage is very effective in the network where it is necessary to understand the contextual information of a given image. In this paper, we propose a method to integrate multiple feature maps in the local region as well as global feature fusion. Local multi-scale feature fusion integrates neighboring feature maps from different levels and scales to get a more diverse range of receptive fields with less computation while keeping detail appearance information. Experimental results demonstrate that the proposed network, which is based on the global and local feature fusion, achieves competitive accuracy with real-time inference speed in semantic segmentation and object detection tasks over the previous state-of-the-art methods. © 2019 IEEE.
URI
http://hdl.handle.net/20.500.11750/10875
DOI
10.1109/IVS.2019.8813786
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
Files:
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Collection:
Division of Automotive Technology2. Conference Papers


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