21 citations · 58 across the 35 of their papers we have counts for
8 papers · 1 filter
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 "…
Prompt Algebra for Task Composition
Pramuditha Perera, Matthew Trager, Luca Zancato +2
We investigate whether prompts learned independently for different tasks can be later combined through prompt algebra to obtain a model that supports composition of tasks. We consi…
Train/Test-Time Adaptation with Retrieval
Luca Zancato, Alessandro Achille, Tian Yu Liu +3
We introduce Train/Test-Time Adaptation with Retrieval (), a method to adapt models both at train and test time by means of a retrieval module and a searchable pool of…
Feature Tracks are not Zero-Mean Gaussian
Stephanie Tsuei, Wenjie Mo, Stefano Soatto
In state estimation algorithms that use feature tracks as input, it is customary to assume that the errors in feature track positions are zero-mean Gaussian. Using a combination of…
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…
Semi-supervised Vision Transformers at Scale
Zhaowei Cai, Avinash Ravichandran, Paolo Favaro +5
We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this…