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

A Critical Look at the Current Usage of Foundation Model for Dense Recognition Task