Dual Mutual Learning Network with Global-local Awareness for RGB-D Salient Object Detection

• URL: http://arxiv.org/abs/2501.01648v1
• Date: Fri, 03 Jan 2025 05:37:54 GMT
• Title: Dual Mutual Learning Network with Global-local Awareness for RGB-D Salient Object Detection
• Authors: Kang Yi, Haoran Tang, Yumeng Li, Jing Xu, Jun Zhang,
• Abstract summary: We propose the GL-DMNet, a novel dual mutual learning network with global-local awareness. We present a position mutual fusion module and a channel mutual fusion module to exploit the interdependencies among different modalities. Our proposed GL-DMNet performs better than 24 RGB-D SOD methods, achieving an average improvement of 3%.
• Score: 10.353412441955436
• License:
• Abstract: RGB-D salient object detection (SOD), aiming to highlight prominent regions of a given scene by jointly modeling RGB and depth information, is one of the challenging pixel-level prediction tasks. Recently, the dual-attention mechanism has been devoted to this area due to its ability to strengthen the detection process. However, most existing methods directly fuse attentional cross-modality features under a manual-mandatory fusion paradigm without considering the inherent discrepancy between the RGB and depth, which may lead to a reduction in performance. Moreover, the long-range dependencies derived from global and local information make it difficult to leverage a unified efficient fusion strategy. Hence, in this paper, we propose the GL-DMNet, a novel dual mutual learning network with global-local awareness. Specifically, we present a position mutual fusion module and a channel mutual fusion module to exploit the interdependencies among different modalities in spatial and channel dimensions. Besides, we adopt an efficient decoder based on cascade transformer-infused reconstruction to integrate multi-level fusion features jointly. Extensive experiments on six benchmark datasets demonstrate that our proposed GL-DMNet performs better than 24 RGB-D SOD methods, achieving an average improvement of ~3% across four evaluation metrics compared to the second-best model (S3Net). Codes and results are available at https://github.com/kingkung2016/GL-DMNet.

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