1 citations · 2 across the 3 of their papers we have counts for
3 papers
Towards Visual Foundational Models of Physical Scenes
Chethan Parameshwara, Alessandro Achille, Matthew Trager +7
We describe a first step towards learning general-purpose visual representations of physical scenes using only image prediction as a training criterion. To do so, we first define "…
Learning Expressive Prompting With Residuals for Vision Transformers
Rajshekhar Das, Yonatan Dukler, Avinash Ravichandran +1
Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model. In t…
A Meta-Learning Approach to Predicting Performance and Data Requirements
Achin Jain, Gurumurthy Swaminathan, Paolo Favaro +8
We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model pe…