The paper measures the spatial quality of CLIP's dense features before and after RLA fine-tuning using unsupervised segmentation with CAUSE on Cityscapes. The original EVA-CLIP achieves 22.6 mIoU, but after fine-tuning with CLIPSelf the mIoU drops to 17.1, and after RegionCLIP it drops further to 16.2. The authors attribute this degradation to the lack of spatial granularity in language supervision, which compromises the model's visual-centric perception. T-SNE visualizations of object-level dense features confirm the loss of spatial structure.
The fine-tuned models are produced by the authors following published RLA recipes; the degradation is measured on a single dataset (Cityscapes) with a single segmentation method (CAUSE).