Salient object detection (SOD) and camouflaged object detection (COD) are related yet distinct binary mapping tasks. These tasks involve multiple modalities, sharing commonalities and unique cues. Existing research often employs intricate task-specific specialist models, potentially leading to redundancy and suboptimal results. We introduce VSCode, a generalist model with novel 2D prompt learning, to jointly address four SOD tasks and three COD tasks. We utilize VST as the foundation model and introduce 2D prompts within the encoder-decoder architecture to learn domain and task-specific knowledge on two separate dimensions. A prompt discrimination loss helps disentangle peculiarities to benefit model optimization. VSCode outperforms state-of-the-art methods across six tasks on 26 datasets and exhibits zero-shot generalization to unseen tasks by combining 2D prompts, such as RGB-D COD.

基于 VSCode 模型和 2D prompt 学习的引入，共同解决了四个显著目标检测任务和三个伪装目标检测任务，实现了在26个数据集上六个任务的最优结果，并通过结合2D prompts，如RGB-D COD，呈现了对未见任务的零样本泛化能力。

VSCode：基于2D提示学习的通用视觉突显和伪装物体检测