Adaptive Low Rank Adaptation of Segment Anything to Salient Object
  Detection
Ruikai Cui Siyuan He Shi Qiu
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.