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Found 15 papers, 11 papers with code
Face2Text revisited: Improved data set and baseline results
To encourage the development of more human-focused descriptions, we developed a new data set of facial descriptions based on the CelebA image data set.
Pre-training Data Quality and Quantity for a Low-Resource Language: New Corpus and BERT Models for Maltese
We also present a newly created corpus for Maltese, and determine the effect that the pre-training data size and domain have on the downstream performance.
On the Language-specificity of Multilingual BERT and the Impact of Fine-tuning
Recent work has shown evidence that the knowledge acquired by multilingual BERT (mBERT) has two components: a language-specific and a language-neutral one.
Automated segmentation of microtomography imaging of Egyptian mummies
Propagation Phase Contrast Synchrotron Microtomography (PPC-SR${\mu}$CT) is the gold standard for non-invasive and non-destructive access to internal structures of archaeological remains.
On Architectures for Including Visual Information in Neural Language Models for Image Description
We also observe that the merge architecture can have its recurrent neural network pre-trained in a text-only language model (transfer learning) rather than be initialised randomly as usual.
Visually grounded generation of entailments from premises
Natural Language Inference (NLI) is the task of determining the semantic relationship between a premise and a hypothesis.
Visuallly Grounded Generation of Entailments from Premises
Natural Language Inference (NLI) is the task of determining the semantic relationship between a premise and a hypothesis.
Transfer learning from language models to image caption generators: Better models may not transfer better
When designing a neural caption generator, a convolutional neural network can be used to extract image features.
Pre-gen metrics: Predicting caption quality metrics without generating captions
Image caption generation systems are typically evaluated against reference outputs.
Quantifying the amount of visual information used by neural caption generators
This paper addresses the sensitivity of neural image caption generators to their visual input.
Grounded Textual Entailment
Capturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics.
Ranked #2 on
Natural Language Inference
on V-SNLI
Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions
To gain a better understanding of the variation we find in face description and the possible issues that this may raise, we also conducted an annotation study on a subset of the corpus.
What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?
This view suggests that the RNN should only be used to encode linguistic features and that only the final representation should be `merged' with the image features at a later stage.
Where to put the Image in an Image Caption Generator
When a recurrent neural network language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in the RNN -- conditioning the language model by `injecting' image features -- or in a layer following the RNN -- conditioning the language model by `merging' image features.
