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Found 6 papers, 3 papers with code
RoViST: Learning Robust Metrics for Visual Storytelling
We measure the reliability of our metric sets by analysing its correlation with human judgement scores on a sample of machine stories obtained from 4 state-of-the-arts models trained on the Visual Storytelling Dataset (VIST).
SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering
To make explicit teaching of the relations between the two modalities, we proposed and integrated two attention modules, namely a scene graph-based semantic relation-aware attention and a positional relation-aware attention.
Optical Character Recognition
Optical Character Recognition (OCR)
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An Analysis of Deep Reinforcement Learning Agents for Text-based Games
Text-based games(TBG) are complex environments which allow users or computer agents to make textual interactions and achieve game goals. In TBG agent design and training process, balancing the efficiency and performance of the agent models is a major challenge.
RoViST:Learning Robust Metrics for Visual Storytelling
We measure the reliability of our metric sets by analysing its correlation with human judgement scores on a sample of machine stories obtained from 4 state-of-the-arts models trained on the Visual Storytelling Dataset (VIST).
Local Interpretations for Explainable Natural Language Processing: A Survey
As the use of deep learning techniques has grown across various fields over the past decade, complaints about the opaqueness of the black-box models have increased, resulting in an increased focus on transparency in deep learning models.
VICTR: Visual Information Captured Text Representation for Text-to-Vision Multimodal Tasks
We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input.
