基于跨层级注意力学习的RGB-T显著目标检测

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DOI:10.13705/j. issn.1671-6841.2023163

RGB-T Salient Object Detection Based on Cross-level Attention Learning

WEI Mingjun 1,2 ,WEI Shuai',LIU Yazhi 1,2 , LI Hui1 (1.College of Artificial Intelligence,North China University of Science and Technology, Tangshan 063210, China;2. Hebei Provincial Key Laboratory of Industrial Inteligent Perception, Tangshan O63210,China)

Abstract: RGB-thermal saliency object detection (RGB-T SOD) aimed to segment common salient regions in both visible light images and corresponding thermal infrared images.To address the problem of insuffcient utilization of cross-level complementary information among existing methods,a cross-level feature attention learning network (CALNet)was proposed for the RGB-T SOD task. Specifically,the network included a cross-level attention learning module(CAL),which used non-local attention to interact cross-level information among multiple modalities and could fuly explore global positions and local details across diffrent modalities and levels.Inaddition,the network also introduced a global information module(GIM)and a multi-interaction module(MIB),both of which could model and explore multi-type information in a layer-by-layer decoding process for more accurate RGB-T SOD. Extensive experiments on public RGB-T datasets demonstrated that the proposed network achieved excellent performance compared with state-of-the-art algorithms in the field.

Key words: multimodal; non-local attention; RGB-T; salient object detection; feature fusion

0 引言

显著目标检测(salient object detection,SOD)旨在从视觉场景中准确地检测和分割最具吸引力的物体,在各种计算机视觉研究中,如视频对象分割、光场图像分割、目标跟踪和实例分割²等,都起着重要作用。(剩余9317字)

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