Existing single-modal and multi-modal salient object detection (SOD) methods
focus on designing specific architectures tailored for their respective tasks.
However, developing completely different models for different tasks leads to
labor and time consumption, as well as high computational and practical
deployment costs. In this paper, we make the first attempt to address both
single-modal and multi-modal SOD in a unified framework called UniSOD.
Nevertheless, assigning appropriate strategies to modality variable inputs is
challenging. To this end, UniSOD learns modality-aware prompts with
task-specific hints through adaptive prompt learning, which are plugged into
the proposed pre-trained baseline SOD model to handle corresponding tasks,
while only requiring few learnable parameters compared to training the entire
model. Each modality-aware prompt is generated from a switchable prompt
generation block, which performs structural switching solely relied on
single-modal and multi-modal inputs. UniSOD achieves consistent performance
improvement on 14 benchmark datasets for RGB, RGB-D, and RGB-T SOD, which
demonstrates that our method effectively and efficiently unifies single-modal
and multi-modal SOD tasks.

用统一的框架 UniSOD 来处理单模态和多模态显著目标检测任务，通过自适应提示学习生成模态感知提示，并将其插入到预训练基准模型中处理相应的任务，同时与训练整个模型相比，只需要较少的可学习参数，取得了对 RGB、RGB-D 和 RGB-T SOD 任务的一致性性能提升。