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cs.CV2025
EMAG: Self-Rectifying Diffusion Sampling with Exponential Moving Average Guidance
Ankit Yadav, Ta Duc Huy, Lingqiao Liu
In diffusion and flow-matching generative models, guidance techniques are widely used to improve sample quality and consistency. Classifier-free guidance (CFG) is the de facto choi…
cs.CV2025
Unified Unsupervised Anomaly Detection via Matching Cost Filtering
Zhe Zhang, Mingxiu Cai, Gaochang Wu +5
Unsupervised anomaly detection (UAD) aims to identify image- and pixel-level anomalies using only normal training data, with wide applications such as industrial inspection and med…
cs.CV2025
Revisiting Vision Language Foundations for No-Reference Image Quality Assessment
Ankit Yadav, Ta Duc Huy, Lingqiao Liu
Large-scale vision language pre-training has recently shown promise for no-reference image-quality assessment (NR-IQA), yet the relative merits of modern Vision Transformer foundat…