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，基于海量跨模态数据训练的大规模模型（通常称为基础模型）在图像识别与生成等诸多领域取得了显著成就。尽管基础模型在其原始应用场景中表现卓越，但其能否有效迁移至其他多样化下游任务仍待明确。本文对当前基于预训练基础模型构建的判别式密集识别方法进行了简要综述，同时针对一种基于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，当前将扩散模型应用于分割任务的方式并非最优解。本研究旨在为未来基础模型在下游任务中的应用探索提供启示。