A Critical Look at the Current Usage of Foundation Model for Dense Recognition Task
This work provides insights for researchers on improving foundation model applications in downstream tasks, but it is incremental as it critiques existing approaches without proposing a new solution.
The paper surveys methods for dense recognition tasks using foundation models and finds that current deployment of diffusion models for segmentation is suboptimal.
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.