Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Saffar, Mohammad"'
Autor:
Bugliarello, Emanuele, Moraldo, Hernan, Villegas, Ruben, Babaeizadeh, Mohammad, Saffar, Mohammad Taghi, Zhang, Han, Erhan, Dumitru, Ferrari, Vittorio, Kindermans, Pieter-Jan, Voigtlaender, Paul
Generating video stories from text prompts is a complex task. In addition to having high visual quality, videos need to realistically adhere to a sequence of text prompts whilst being consistent throughout the frames. Creating a benchmark for video g
Externí odkaz:
http://arxiv.org/abs/2308.11606
Autor:
Villegas, Ruben, Babaeizadeh, Mohammad, Kindermans, Pieter-Jan, Moraldo, Hernan, Zhang, Han, Saffar, Mohammad Taghi, Castro, Santiago, Kunze, Julius, Erhan, Dumitru
We present Phenaki, a model capable of realistic video synthesis, given a sequence of textual prompts. Generating videos from text is particularly challenging due to the computational cost, limited quantities of high quality text-video data and varia
Externí odkaz:
http://arxiv.org/abs/2210.02399
We present Answer-Me, a task-aware multi-task framework which unifies a variety of question answering tasks, such as, visual question answering, visual entailment, visual reasoning. In contrast to previous works using contrastive or generative captio
Externí odkaz:
http://arxiv.org/abs/2205.00949
We propose FindIt, a simple and versatile framework that unifies a variety of visual grounding and localization tasks including referring expression comprehension, text-based localization, and object detection. Key to our architecture is an efficient
Externí odkaz:
http://arxiv.org/abs/2203.17273
Autor:
Babaeizadeh, Mohammad, Saffar, Mohammad Taghi, Nair, Suraj, Levine, Sergey, Finn, Chelsea, Erhan, Dumitru
An agent that is capable of predicting what happens next can perform a variety of tasks through planning with no additional training. Furthermore, such an agent can internally represent the complex dynamics of the real-world and therefore can acquire
Externí odkaz:
http://arxiv.org/abs/2106.13195
Autor:
Babaeizadeh, Mohammad, Saffar, Mohammad Taghi, Hafner, Danijar, Kannan, Harini, Finn, Chelsea, Levine, Sergey, Erhan, Dumitru
Model-based reinforcement learning (MBRL) methods have shown strong sample efficiency and performance across a variety of tasks, including when faced with high-dimensional visual observations. These methods learn to predict the environment dynamics a
Externí odkaz:
http://arxiv.org/abs/2012.04603
Self-attention has recently been adopted for a wide range of sequence modeling problems. Despite its effectiveness, self-attention suffers from quadratic compute and memory requirements with respect to sequence length. Successful approaches to reduce
Externí odkaz:
http://arxiv.org/abs/2003.05997
This paper presents an accurate method for verifying online signatures. The main difficulty of signature verification come from: (1) Lacking enough training samples (2) The methods must be spatial change invariant. To deal with these difficulties and
Externí odkaz:
http://arxiv.org/abs/1806.09986
This paper gives an overview on semantic segmentation consists of an explanation of this field, it's status and relation with other vision fundamental tasks, different datasets and common evaluation parameters that have been used by researchers. This
Externí odkaz:
http://arxiv.org/abs/1806.06172
Autor:
Farhadi, Roya, Saffar, Mohammad Jafar, Monfared, Fatemeh Tarighat, Larijani, Laleh Vahedi, Kenari, Saeid Abedian, Charati, Jamshid Yazdani
Publikováno v:
In Journal of Global Antimicrobial Resistance September 2022 30:474-479
