[D] [R] Are there any methods/works that enable extracting high-quality dense feature map from CLIP/OpenCLIP image encoders without large scale finetuning?

Hi, as stated in the title, I'm curious if such methods exist. We know that (trained) CLIP's image and text encoders both output an 1D vector that are aligned in the latent space, which allows to easily compute the similarities between a batch of images and texts. However, in many vision applications, it is desirable to get a 3D feature map of shape C\*H\*W. Ideally, if the vector at each spatial location in this feature map is as high-quality as …

clip compute feature finetuning image machinelearning map quality scale space text vector