3 citations · 9 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
One-Shot Face Video Re-enactment using Hybrid Latent Spaces of StyleGAN2
Trevine Oorloff, Yaser Yacoob
While recent research has progressively overcome the low-resolution constraint of one-shot face video re-enactment with the help of StyleGAN's high-fidelity portrait generation, th…
Expressive Talking Head Video Encoding in StyleGAN2 Latent-Space
Trevine Oorloff, Yaser Yacoob
While the recent advances in research on video reenactment have yielded promising results, the approaches fall short in capturing the fine, detailed, and expressive facial features…