  The In-Context Learning (ICL) is to understand a new task via a few
demonstrations (aka. prompt) and predict new inputs without tuning the models.
While it has been widely studied in NLP, it is still a relatively new area of
research in computer vision. To reveal the factors influencing the performance
of visual in-context learning, this paper shows that prompt selection and
prompt fusion are two major factors that have a direct impact on the inference
performance of visual context learning. Prompt selection is the process of
identifying the most appropriate prompt or example to help the model understand
new tasks. This is important because providing the model with relevant prompts
can help it learn more effectively and efficiently. Prompt fusion involves
combining knowledge from different positions within the large-scale visual
model. By doing this, the model can leverage the diverse knowledge stored in
different parts of the model to improve its performance on new tasks. Based
these findings, we propose a simple framework prompt-SelF for visual in-context
learning. Specifically, we first use the pixel-level retrieval method to select
a suitable prompt, and then use different prompt fusion methods to activate all
the knowledge stored in the large-scale model, and finally ensemble the
prediction results obtained from different prompt fusion methods to obtain the
final prediction results. And we conduct extensive experiments on single-object
segmentation and detection tasks to demonstrate the effectiveness of
prompt-SelF. Remarkably, the prompt-SelF has outperformed OSLSM based
meta-learning in 1-shot segmentation for the first time. This indicated the
great potential of visual in-context learning. The source code and models will
be available at \url{https://github.com/syp2ysy/prompt-SelF}.
