3 citations · 5 across the 3 of their papers we have counts for
9 papers
Face2Text revisited: Improved data set and baseline results
Marc Tanti, Shaun Abdilla, Adrian Muscat +3
Current image description generation models do not transfer well to the task of describing human faces. To encourage the development of more human-focused descriptions, we develope…
On Architectures for Including Visual Information in Neural Language Models for Image Description
Marc Tanti, Albert Gatt, Kenneth P. Camilleri
A neural language model can be conditioned into generating descriptions for images by providing visual information apart from the sentence prefix. This visual information can be in…
Visuallly Grounded Generation of Entailments from Premises
Somaye Jafaritazehjani, Albert Gatt, Marc Tanti
Natural Language Inference (NLI) is the task of determining the semantic relationship between a premise and a hypothesis. In this paper, we focus on the {\em generation} of hypothe…
Transfer learning from language models to image caption generators: Better models may not transfer better
Marc Tanti, Albert Gatt, Kenneth P. Camilleri
When designing a neural caption generator, a convolutional neural network can be used to extract image features. Is it possible to also use a neural language model to extract sente…
Quantifying the amount of visual information used by neural caption generators
Marc Tanti, Albert Gatt, Kenneth P. Camilleri
This paper addresses the sensitivity of neural image caption generators to their visual input. A sensitivity analysis and omission analysis based on image foils is reported, showin…
Pre-gen metrics: Predicting caption quality metrics without generating captions
Marc Tanti, Albert Gatt, Adrian Muscat
Image caption generation systems are typically evaluated against reference outputs. We show that it is possible to predict output quality without generating the captions, based on…