10 citations · 27 across the 25 of their papers we have counts for
6 papers · 1 filter
Descriminative-Generative Custom Tokens for Vision-Language Models
Pramuditha Perera, Matthew Trager, Luca Zancato +2
This paper explores the possibility of learning custom tokens for representing new concepts in Vision-Language Models (VLMs). Our aim is to learn tokens that can be effective for b…
Multi-Modal Hallucination Control by Visual Information Grounding
Alessandro Favero, Luca Zancato, Matthew Trager +5
Generative Vision-Language Models (VLMs) are prone to generate plausible-sounding textual answers that, however, are not always grounded in the input image. We investigate this phe…
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes
Abdelhak Lemkhenter, Manchen Wang, Luca Zancato +3
In this paper we introduce SemiGPC, a distribution-aware label refinement strategy based on Gaussian Processes where the predictions of the model are derived from the labels poster…
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…
A linearized framework and a new benchmark for model selection for fine-tuning
Aditya Deshpande, Alessandro Achille, Avinash Ravichandran +6
Fine-tuning from a collection of models pre-trained on different domains (a "model zoo") is emerging as a technique to improve test accuracy in the low-data regime. However, model…