SVL uses vision-language alignment via scene-level shadow ratio regression and global-to-local coupling on a frozen DINOv3 encoder to disambiguate shadows from dark surfaces in dense prediction.
Vision-language models for vision tasks: A survey
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MFVLR uses multi-domain vision-language reconstruction with a fine-grained language transformer, multi-domain vision encoder, and vision injection module to achieve generalizable detection and localization of diffusion-synthesized face forgeries.
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Revisiting Shadow Detection from a Vision-Language Perspective
SVL uses vision-language alignment via scene-level shadow ratio regression and global-to-local coupling on a frozen DINOv3 encoder to disambiguate shadows from dark surfaces in dense prediction.
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MFVLR: Multi-domain Fine-grained Vision-Language Reconstruction for Generalizable Diffusion Face Forgery Detection and Localization
MFVLR uses multi-domain vision-language reconstruction with a fine-grained language transformer, multi-domain vision encoder, and vision injection module to achieve generalizable detection and localization of diffusion-synthesized face forgeries.