Foundation models, such as OpenAI's GPT-3 and GPT-4, Meta's LLaMA, and
Google's PaLM2, have revolutionized the field of artificial intelligence. A
notable paradigm shift has been the advent of the Segment Anything Model (SAM),
which has exhibited a remarkable capability to segment real-world objects,
trained on 1 billion masks and 11 million images. Although SAM excels in
general object segmentation, it lacks the intrinsic ability to detect salient
objects, resulting in suboptimal performance in this domain. To address this
challenge, we present the Segment Salient Object Model (SSOM), an innovative
approach that adaptively fine-tunes SAM for salient object detection by
harnessing the low-rank structure inherent in deep learning. Comprehensive
qualitative and quantitative evaluations across five challenging RGB benchmark
datasets demonstrate the superior performance of our approach, surpassing
state-of-the-art methods.

基于深度学习中内在的低秩结构，我们提出了一种创新的方法，通过自适应微调 Segment Anything Model (SAM) 来实现显著目标检测，通过在五个具有挑战性的 RGB 基准数据集上进行全面的定性和定量评估，证明了我们方法的卓越性能，超越了最先进的方法。