Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Navasardyan, Shant"'
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 textual prompts. Our method utilizes object masks and individual description
Externí odkaz:
http://arxiv.org/abs/2406.04032
Autor:
Zohranyan, Vazgen, Navasardyan, Vagner, Navasardyan, Hayk, Borggrefe, Jan, Navasardyan, Shant
Recent advancements in AI have significantly transformed medical imaging, particularly in angiography, by enhancing diagnostic precision and patient care. However existing works are limited in analyzing the aorta and iliac arteries, above all for vas
Externí odkaz:
http://arxiv.org/abs/2404.17029
Autor:
Henschel, Roberto, Khachatryan, Levon, Hayrapetyan, Daniil, Poghosyan, Hayk, Tadevosyan, Vahram, Wang, Zhangyang, Navasardyan, Shant, Shi, Humphrey
Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, making it easy to create diverse and individual content. However, existing approaches mostly focus on high-quality short video generation (typi
Externí odkaz:
http://arxiv.org/abs/2403.14773
Autor:
Manukyan, Hayk, Sargsyan, Andranik, Atanyan, Barsegh, Wang, Zhangyang, Navasardyan, Shant, Shi, Humphrey
Recent progress in text-guided image inpainting, based on the unprecedented success of text-to-image diffusion models, has led to exceptionally realistic and visually plausible results. However, there is still significant potential for improvement in
Externí odkaz:
http://arxiv.org/abs/2312.14091
Autor:
Li, Jiachen, Henschel, Roberto, Goel, Vidit, Ohanyan, Marianna, Navasardyan, Shant, Shi, Humphrey
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 segmentation provides time-consistent instance masks, results are unsatisfactory
Externí odkaz:
http://arxiv.org/abs/2311.04212
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 to generate compositional multi-concept images naturally, limiting th
Externí odkaz:
http://arxiv.org/abs/2310.07419
Autor:
Khachatryan, Levon, Movsisyan, Andranik, Tadevosyan, Vahram, Henschel, Roberto, Wang, Zhangyang, Navasardyan, Shant, Shi, Humphrey
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-to-video generation and propose a low-cost approach (without any traini
Externí odkaz:
http://arxiv.org/abs/2303.13439
Autor:
Jiao, Siyu, Zhang, Gengwei, Navasardyan, Shant, Chen, Ling, Zhao, Yao, Wei, Yunchao, Shi, Humphrey
In this paper, we aim to tackle the challenging few-shot segmentation task from a new perspective. Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain s
Externí odkaz:
http://arxiv.org/abs/2301.01208
Autor:
Xu, Xingqian, Navasardyan, Shant, Tadevosyan, Vahram, Sargsyan, Andranik, Mu, Yadong, Shi, Humphrey
Image completion with large-scale free-form missing regions is one of the most challenging tasks for the computer vision community. While researchers pursue better solutions, drawbacks such as pattern unawareness, blurry textures, and structure disto
Externí odkaz:
http://arxiv.org/abs/2211.03700
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 networks (CNN) for the past few years, which have become the de-facto
Externí odkaz:
http://arxiv.org/abs/2208.12801