10 citations · 14 across the 3 of their papers we have counts for
7 papers
Improving Super-Resolution Performance using Meta-Attention Layers
Matthew Aquilina, Christian Galea, John Abela +2
Convolutional Neural Networks (CNNs) have achieved impressive results across many super-resolution (SR) and image restoration tasks. While many such networks can upscale low-resolu…
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…
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…
Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions
Albert Gatt, Marc Tanti, Adrian Muscat +6
The past few years have witnessed renewed interest in NLP tasks at the interface between vision and language. One intensively-studied problem is that of automatically generating te…