CLIP models understand 360-degree textual semantics via explicit identifiers but show limited comprehension of visual semantics under horizontal circular shifts, which a LoRA fine-tuning approach improves with a noted trade-off in original task performance.
Diff- pano: Scalable and consistent text to panorama generation with spherical epipolar-aware diffusion.arXiv preprint arXiv:2410.24203
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Probing CLIP's Comprehension of 360-Degree Textual and Visual Semantics
CLIP models understand 360-degree textual semantics via explicit identifiers but show limited comprehension of visual semantics under horizontal circular shifts, which a LoRA fine-tuning approach improves with a noted trade-off in original task performance.