  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.
Source code has been available at https://github.com/Sssssuperior/VSCode.
