VST++: Efficient and Stronger Visual Saliency Transformer

• URL: http://arxiv.org/abs/2310.11725v2
• Date: Thu, 11 Apr 2024 08:11:20 GMT
• Title: VST++: Efficient and Stronger Visual Saliency Transformer
• Authors: Nian Liu, Ziyang Luo, Ni Zhang, Junwei Han,
• Abstract summary: We develop an efficient and stronger VST++ model to explore global long-range dependencies. We evaluate our model across various transformer-based backbones on RGB, RGB-D, and RGB-T SOD benchmark datasets.
• Score: 74.26078624363274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While previous CNN-based models have exhibited promising results for salient object detection (SOD), their ability to explore global long-range dependencies is restricted. Our previous work, the Visual Saliency Transformer (VST), addressed this constraint from a transformer-based sequence-to-sequence perspective, to unify RGB and RGB-D SOD. In VST, we developed a multi-task transformer decoder that concurrently predicts saliency and boundary outcomes in a pure transformer architecture. Moreover, we introduced a novel token upsampling method called reverse T2T for predicting a high-resolution saliency map effortlessly within transformer-based structures. Building upon the VST model, we further propose an efficient and stronger VST version in this work, i.e. VST++. To mitigate the computational costs of the VST model, we propose a Select-Integrate Attention (SIA) module, partitioning foreground into fine-grained segments and aggregating background information into a single coarse-grained token. To incorporate 3D depth information with low cost, we design a novel depth position encoding method tailored for depth maps. Furthermore, we introduce a token-supervised prediction loss to provide straightforward guidance for the task-related tokens. We evaluate our VST++ model across various transformer-based backbones on RGB, RGB-D, and RGB-T SOD benchmark datasets. Experimental results show that our model outperforms existing methods while achieving a 25% reduction in computational costs without significant performance compromise. The demonstrated strong ability for generalization, enhanced performance, and heightened efficiency of our VST++ model highlight its potential.

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