Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Bar, Amir"'
Autor:
Bar, Amir, Bakhtiar, Arya, Tran, Danny, Loquercio, Antonio, Rajasegaran, Jathushan, LeCun, Yann, Globerson, Amir, Darrell, Trevor
Animals perceive the world to plan their actions and interact with other agents to accomplish complex tasks, demonstrating capabilities that are still unmatched by AI systems. To advance our understanding and reduce the gap between the capabilities o
Externí odkaz:
http://arxiv.org/abs/2404.09991
Visual Prompting is a technique for teaching models to perform a visual task via in-context examples, without any additional training. In this work, we analyze the activations of MAE-VQGAN, a recent Visual Prompting model, and find task vectors, acti
Externí odkaz:
http://arxiv.org/abs/2404.05729
Autor:
Xu, Jiarui, Gandelsman, Yossi, Bar, Amir, Yang, Jianwei, Gao, Jianfeng, Darrell, Trevor, Wang, Xiaolong
In-context learning allows adapting a model to new tasks given a task description at test time. In this paper, we present IMProv - a generative model that is able to in-context learn visual tasks from multimodal prompts. Given a textual description o
Externí odkaz:
http://arxiv.org/abs/2312.01771
Autor:
Bai, Yutong, Geng, Xinyang, Mangalam, Karttikeya, Bar, Amir, Yuille, Alan, Darrell, Trevor, Malik, Jitendra, Efros, Alexei A
We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data. To do this, we define a common format, "visual sentences", in which we can represent raw images and videos
Externí odkaz:
http://arxiv.org/abs/2312.00785
Autor:
Bar, Amir, Bordes, Florian, Shocher, Assaf, Assran, Mahmoud, Vincent, Pascal, Ballas, Nicolas, Darrell, Trevor, Globerson, Amir, LeCun, Yann
Masked Image Modeling (MIM) is a promising self-supervised learning approach that enables learning from unlabeled images. Despite its recent success, learning good representations through MIM remains challenging because it requires predicting the rig
Externí odkaz:
http://arxiv.org/abs/2308.00566
Autor:
Balestriero, Randall, Ibrahim, Mark, Sobal, Vlad, Morcos, Ari, Shekhar, Shashank, Goldstein, Tom, Bordes, Florian, Bardes, Adrien, Mialon, Gregoire, Tian, Yuandong, Schwarzschild, Avi, Wilson, Andrew Gordon, Geiping, Jonas, Garrido, Quentin, Fernandez, Pierre, Bar, Amir, Pirsiavash, Hamed, LeCun, Yann, Goldblum, Micah
Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning. Yet, much like cooking, training SSL methods is a delicate art with a high barrier to entry. While many components are familiar, succes
Externí odkaz:
http://arxiv.org/abs/2304.12210
How does one adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification? Inspired by prompting in NLP, this paper investigates visual prompting: given input-output image example(s) of a new t
Externí odkaz:
http://arxiv.org/abs/2209.00647
Autor:
Ben-Avraham, Elad, Herzig, Roei, Mangalam, Karttikeya, Bar, Amir, Rohrbach, Anna, Karlinsky, Leonid, Darrell, Trevor, Globerson, Amir
This technical report describes the SViT approach for the Ego4D Point of No Return (PNR) Temporal Localization Challenge. We propose a learning framework StructureViT (SViT for short), which demonstrates how utilizing the structure of a small number
Externí odkaz:
http://arxiv.org/abs/2206.07689
Autor:
Ben-Avraham, Elad, Herzig, Roei, Mangalam, Karttikeya, Bar, Amir, Rohrbach, Anna, Karlinsky, Leonid, Darrell, Trevor, Globerson, Amir
Recent action recognition models have achieved impressive results by integrating objects, their locations and interactions. However, obtaining dense structured annotations for each frame is tedious and time-consuming, making these methods expensive t
Externí odkaz:
http://arxiv.org/abs/2206.06346
Publikováno v:
In Geomorphology 15 November 2024 465
