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arXiv · 2108.03990

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

Zhengyi Liu·Yuan Wang·Zhengzheng Tu·Yun Xiao·Bin Tang
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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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Zhengyi Liu, Yuan Wang, Zhengzheng Tu, Yun Xiao, Bin Tang. 2021-08-09. TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network. https://doi.org/10.1145/3474085.3475601

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