Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Saini, Nirat"'
We propose WayEx, a new method for learning complex goal-conditioned robotics tasks from a single demonstration. Our approach distinguishes itself from existing imitation learning methods by demanding fewer expert examples and eliminating the need fo
Externí odkaz:
http://arxiv.org/abs/2407.15849
Autor:
Saini, Nirat, Bodla, Navaneeth, Shrivastava, Ashish, Ravichandran, Avinash, Zhang, Xiao, Shrivastava, Abhinav, Singh, Bharat
We introduce InVi, an approach for inserting or replacing objects within videos (referred to as inpainting) using off-the-shelf, text-to-image latent diffusion models. InVi targets controlled manipulation of objects and blending them seamlessly into
Externí odkaz:
http://arxiv.org/abs/2407.10958
Autor:
Saini, Nirat, Wang, Hanyu, Swaminathan, Archana, Jayasundara, Vinoj, He, Bo, Gupta, Kamal, Shrivastava, Abhinav
Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions. In this paper, we study the task of cutting objects in different styles and the resulting object state changes. We
Externí odkaz:
http://arxiv.org/abs/2309.14339
We study the problem of compositional zero-shot learning for object-attribute recognition. Prior works use visual features extracted with a backbone network, pre-trained for object classification and thus do not capture the subtly distinct features a
Externí odkaz:
http://arxiv.org/abs/2205.08536
Current action recognition systems require large amounts of training data for recognizing an action. Recent works have explored the paradigm of zero-shot and few-shot learning to learn classifiers for unseen categories or categories with few labels.
Externí odkaz:
http://arxiv.org/abs/2008.12432
Researchers have observed that Visual Question Answering (VQA) models tend to answer questions by learning statistical biases in the data. For example, their answer to the question "What is the color of the grass?" is usually "Green", whereas a quest
Externí odkaz:
http://arxiv.org/abs/1811.07789