8 citations · 15 across the 6 of their papers we have counts for
6 papers
Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis
Marianna Ohanyan, Hayk Manukyan, Zhangyang Wang +2
We present Zero-Painter, a novel training-free framework for layout-conditional text-to-image synthesis that facilitates the creation of detailed and controlled imagery from textua…
Dr-SAM: An End-to-End Framework for Vascular Segmentation, Diameter Estimation, and Anomaly Detection on Angiography Images
Vazgen Zohranyan, Vagner Navasardyan, Hayk Navasardyan +2
Recent advancements in AI have significantly transformed medical imaging, particularly in angiography, by enhancing diagnostic precision and patient care. However existing works ar…
Video Instance Matting
Jiachen Li, Roberto Henschel, Vidit Goel +3
Conventional video matting outputs one alpha matte for all instances appearing in a video frame so that individual instances are not distinguished. While video instance segmentatio…
Multi-Concept T2I-Zero: Tweaking Only The Text Embeddings and Nothing Else
Hazarapet Tunanyan, Dejia Xu, Shant Navasardyan +2
Recent advances in text-to-image diffusion models have enabled the photorealistic generation of images from text prompts. Despite the great progress, existing models still struggle…
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan +4
Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-t…
VMFormer: End-to-End Video Matting with Transformer
Jiachen Li, Vidit Goel, Marianna Ohanyan +3
Video matting aims to predict the alpha mattes for each frame from a given input video sequence. Recent solutions to video matting have been dominated by deep convolutional neural…