3 papers
cs.CV2025
Stable Diffusion Models are Secretly Good at Visual In-Context Learning
Trevine Oorloff, Vishwanath Sindagi, Wele Gedara Chaminda Bandara +4
Large language models (LLM) in natural language processing (NLP) have demonstrated great potential for in-context learning (ICL) -- the ability to leverage a few sets of example pr…
cs.CV2025
Mitigating Hallucinations in Diffusion Models through Adaptive Attention Modulation
Trevine Oorloff, Yaser Yacoob, Abhinav Shrivastava
Diffusion models, while increasingly adept at generating realistic images, are notably hindered by hallucinations -- unrealistic or incorrect features inconsistent with the trained…
cs.CV2024
AVFF: Audio-Visual Feature Fusion for Video Deepfake Detection
Trevine Oorloff, Surya Koppisetti, Nicolò Bonettini +5
With the rapid growth in deepfake video content, we require improved and generalizable methods to detect them. Most existing detection methods either use uni-modal cues or rely on…