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cs.CV2025
Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption
Kazuki Adachi, Shin'ya Yamaguchi, Tomoki Hamagami
Pre-trained vision-language models such as contrastive language-image pre-training (CLIP) have demonstrated a remarkable generalizability, which has enabled a wide range of applica…
cs.CV2024
Latent Denoising Diffusion GAN: Faster sampling, Higher image quality
Luan Thanh Trinh, Tomoki Hamagami
Diffusion models are emerging as powerful solutions for generating high-fidelity and diverse images, often surpassing GANs under many circumstances. However, their slow inference s…