MTFNet: Mutual-Transformer Fusion Network for RGB-D Salient Object Detection

• URL: http://arxiv.org/abs/2112.01177v1
• Date: Thu, 2 Dec 2021 12:48:37 GMT
• Title: MTFNet: Mutual-Transformer Fusion Network for RGB-D Salient Object Detection
• Authors: Xixi Wang, Bo Jiang, Xiao Wang, Bin Luo
• Abstract summary: We propose a novel Mutual-Transformer Fusion Network (MTFNet) for RGB-D SOD. MTFNet contains two main modules, $i.e.$, Focal Feature Extractor (FFE) and Mutual-Transformer Fusion (MTF) Comprehensive experimental results on six public benchmarks demonstrate the superiority of our proposed MTFNet.
• Score: 15.371153771528093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Salient object detection (SOD) on RGB-D images is an active problem in computer vision. The main challenges for RGB-D SOD problem are how to 1) extract the accurate features for RGB and Depth image data with clutter background or poor image quality and 2) explore the complementary information between RGB and Depth image data. To address these challenges, we propose a novel Mutual-Transformer Fusion Network (MTFNet) for RGB-D SOD. MTFNet contains two main modules, $i.e.$, Focal Feature Extractor (FFE) and Mutual-Transformer Fusion (MTF). FFE aims to extract the more accurate CNN features for RGB and Depth images by introducing a novel pixel-level focal regularization to guide CNN feature extractor. MTF is designed to deeply exploit the multi-modal interaction between RGB and Depth images on both coarse and fine scales. The main benefit of MTF is that it conducts the learning of intra-modality and inter-modality simultaneously and thus can achieve communication across different modalities more directly and sufficiently. Comprehensive experimental results on six public benchmarks demonstrate the superiority of our proposed MTFNet.

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