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Found 8 papers, 8 papers with code
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks
To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: A) generate and train a standard SR network on synthetic low-resolution - high-resolution (LR - HR) pairs or B) attempt to predict the degradations an LR image has suffered and use these to inform a customised SR network.
Improving Super-Resolution Performance using Meta-Attention Layers
While many such networks can upscale low-resolution (LR) images using just the raw pixel-level information, the ill-posed nature of SR can make it difficult to accurately super-resolve an image which has undergone multiple different degradations.
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.
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.
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.
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.
