TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network

• URL: http://arxiv.org/abs/2108.03990v1
• Date: Mon, 9 Aug 2021 12:42:56 GMT
• Title: TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network
• Authors: Zhengyi Liu, Yuan Wang, Zhengzheng Tu, Yun Xiao, Bin Tang
• Abstract summary: We propose a triplet transformer embedding module to enhance multi-level features. It is the first to use three transformer encoders with shared weights to enhance multi-level features. The proposed triplet transformer embedding network (TriTransNet) achieves the state-of-the-art performance in RGB-D salient object detection.
• Score: 18.910883028990998
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Salient object detection is the pixel-level dense prediction task which can highlight the prominent object in the scene. Recently U-Net framework is widely used, and continuous convolution and pooling operations generate multi-level features which are complementary with each other. In view of the more contribution of high-level features for the performance, we propose a triplet transformer embedding module to enhance them by learning long-range dependencies across layers. It is the first to use three transformer encoders with shared weights to enhance multi-level features. By further designing scale adjustment module to process the input, devising three-stream decoder to process the output and attaching depth features to color features for the multi-modal fusion, the proposed triplet transformer embedding network (TriTransNet) achieves the state-of-the-art performance in RGB-D salient object detection, and pushes the performance to a new level. Experimental results demonstrate the effectiveness of the proposed modules and the competition of TriTransNet.

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