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DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models
Simone Carnemolla, Matteo Pennisi, Sarinda Samarasinghe +5
Understanding and explaining the behavior of machine learning models is essential for building transparent and trustworthy AI systems. We introduce DEXTER, a data-free framework th…
From Frames to Clips: Training-free Adaptive Key Clip Selection for Long-Form Video Understanding
Guangyu Sun, Archit Singhal, Burak Uzkent +3
Video Large Language Models (VLMs) have achieved strong performance on various vision-language tasks, yet their practical use is limited by the massive number of visual tokens prod…
Enhancing Privacy-Utility Trade-offs to Mitigate Memorization in Diffusion Models
Chen Chen, Daochang Liu, Mubarak Shah +1
Text-to-image diffusion models have demonstrated remarkable capabilities in creating images highly aligned with user prompts, yet their proclivity for memorizing training set image…
Generative Physical AI in Vision: A Survey
Daochang Liu, Junyu Zhang, Anh-Dung Dinh +5
Generative Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication…
Investigating Memorization in Video Diffusion Models
Chen Chen, Enhuai Liu, Daochang Liu +2
Diffusion models, widely used for image and video generation, face a significant limitation: the risk of memorizing and reproducing training data during inference, potentially gene…
Exploring Local Memorization in Diffusion Models via Bright Ending Attention
Chen Chen, Daochang Liu, Mubarak Shah +1
Text-to-image diffusion models have achieved unprecedented proficiency in generating realistic images. However, their inherent tendency to memorize and replicate training data duri…