Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Sadeghian, Amir"'
Although text-to-image diffusion models have made significant strides in generating images from text, they are sometimes more inclined to generate images like the data on which the model was trained rather than the provided text. This limitation has
Externí odkaz:
http://arxiv.org/abs/2304.04968
We present a conceptually simple self-supervised method for saliency detection. Our method generates and uses pseudo-ground truth labels for training. The generated pseudo-GT labels don't require any kind of human annotations (e.g., pixel-wise labels
Externí odkaz:
http://arxiv.org/abs/2203.04478
Autor:
Sadeghian, Amir M., Dashti, Farzad, Shariati, Behnam, Mokhtare, Marjan, Sotoudeheian, Mohammadjavad
Publikováno v:
In Clinics and Research in Hepatology and Gastroenterology May 2024 48(5)
Autor:
Shenoi, Abhijeet, Patel, Mihir, Gwak, JunYoung, Goebel, Patrick, Sadeghian, Amir, Rezatofighi, Hamid, Martín-Martín, Roberto, Savarese, Silvio
Robots navigating autonomously need to perceive and track the motion of objects and other agents in its surroundings. This information enables planning and executing robust and safe trajectories. To facilitate these processes, the motion should be pe
Externí odkaz:
http://arxiv.org/abs/2002.08397
Autor:
Martín-Martín, Roberto, Patel, Mihir, Rezatofighi, Hamid, Shenoi, Abhijeet, Gwak, JunYoung, Frankel, Eric, Sadeghian, Amir, Savarese, Silvio
We present JRDB, a novel egocentric dataset collected from our social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360$^\circ$ RGB video at 15 fps, 3D point clouds fro
Externí odkaz:
http://arxiv.org/abs/1910.11792
Autor:
Mokhtare, Marjan, Abdi, Arman, Sadeghian, Amir M., Sotoudeheian, Mohammadjavad, Namazi, Abolfazl, Khalighi Sikaroudi, Masoumeh
Publikováno v:
In Clinical Nutrition ESPEN December 2023 58:221-227
Autor:
Kosaraju, Vineet, Sadeghian, Amir, Martín-Martín, Roberto, Reid, Ian, Rezatofighi, S. Hamid, Savarese, Silvio
Predicting the future trajectories of multiple interacting agents in a scene has become an increasingly important problem for many different applications ranging from control of autonomous vehicles and social robots to security and surveillance. This
Externí odkaz:
http://arxiv.org/abs/1907.03395
Directed information (DI) is a useful tool to explore time-directed interactions in multivariate data. However, as originally formulated DI is not well suited to interactions that change over time. In previous work, adaptive directed information was
Externí odkaz:
http://arxiv.org/abs/1906.10746
Autor:
Pokle, Ashwini, Martín-Martín, Roberto, Goebel, Patrick, Chow, Vincent, Ewald, Hans M., Yang, Junwei, Wang, Zhenkai, Sadeghian, Amir, Sadigh, Dorsa, Savarese, Silvio, Vázquez, Marynel
Publikováno v:
2019 International Conference on Robotics and Automation (ICRA)
We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a goal, and a deep local trajectory planner and velocity controller to c
Externí odkaz:
http://arxiv.org/abs/1905.05279
Humans can routinely follow a trajectory defined by a list of images/landmarks. However, traditional robot navigation methods require accurate mapping of the environment, localization, and planning. Moreover, these methods are sensitive to subtle cha
Externí odkaz:
http://arxiv.org/abs/1903.02749