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.

翻译：近年来，在海量跨模态数据上训练的大模型（通常被称为基础模型）在图像识别与生成等多个领域取得了显著成就。尽管这些模型在原始应用场景中表现优异，但其能否有效迁移至其他不同的下游任务尚不明确。本文针对当前基于预训练基础模型的判别性密集识别任务方法进行了简短综述，并对一种基于Stable Diffusion的现有开放词汇分割方法开展了初步实验分析，结果表明当前将扩散模型部署于分割任务的方式并非最优。本研究旨在为未来将基础模型应用于下游任务的研究提供启示。