External Prompt Features Enhanced Parameter-efficient Fine-tuning for Salient Object Detection
Wen Liang, Peipei Ran, Mengchao Bai, Xiao Liu, P. Bilha Githinji, Wei, Zhao, Peiwu Qin

TL;DR
This paper introduces ExPert, a parameter-efficient fine-tuning approach for transformer-based salient object detection that incorporates external prompt features, achieving superior performance with fewer training parameters.
Contribution
The paper presents a novel fine-tuning method with adapters and injectors for transformers, enhancing SOD performance while reducing training complexity.
Findings
ExPert surpasses previous SOTA models on five datasets.
Achieves 0.215 MAE on ECSSD with 80.2M parameters.
Outperforms SelfReformer by 21% and EGNet by 47%.
Abstract
Salient object detection (SOD) aims at finding the most salient objects in images and outputs pixel-level binary masks. Transformer-based methods achieve promising performance due to their global semantic understanding, crucial for identifying salient objects. However, these models tend to be large and require numerous training parameters. To better harness the potential of transformers for SOD, we propose a novel parameter-efficient fine-tuning method aimed at reducing the number of training parameters while enhancing the salient object detection capability. Our model, termed EXternal Prompt features Enhanced adapteR Tuning (ExPert), features an encoder-decoder structure with adapters and injectors interspersed between the layers of a frozen transformer encoder. The adapter modules adapt the pretrained backbone to SOD while the injector modules incorporate external prompt features to…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Infrared Target Detection Methodologies · Advanced Neural Network Applications
MethodsAdapter
