  In recent years large model trained on huge amount of cross-modality data,
which is usually be termed as foundation model, achieves conspicuous
accomplishment in many fields, such as image recognition and generation. Though
achieving great success in their original application case, it is still unclear
whether those foundation models can be applied to other different downstream
tasks. In this paper, we conduct a short survey on the current methods for
discriminative dense recognition tasks, which are built on the pretrained
foundation model. And we also provide some preliminary experimental analysis of
an existing open-vocabulary segmentation method based on Stable Diffusion,
which indicates the current way of deploying diffusion model for segmentation
is not optimal. This aims to provide insights for future research on adopting
foundation model for downstream task.
