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Found 6 papers, 2 papers with code
Game-MUG: Multimodal Oriented Game Situation Understanding and Commentary Generation Dataset
In addition, we also propose a new audience conversation augmented commentary dataset by covering the game situation and audience conversation understanding, and introducing a robust joint multimodal dual learning model as a baseline.
PEACH: Pretrained-embedding Explanation Across Contextual and Hierarchical Structure
In this work, we propose a novel tree-based explanation technique, PEACH (Pretrained-embedding Explanation Across Contextual and Hierarchical Structure), that can explain how text-based documents are classified by using any pretrained contextual embeddings in a tree-based human-interpretable manner.
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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In-game Toxic Language Detection: Shared Task and Attention Residuals
In-game toxic language becomes the hot potato in the gaming industry and community.
Understanding Attention for Vision-and-Language Tasks
Attention mechanism has been used as an important component across Vision-and-Language(VL) tasks in order to bridge the semantic gap between visual and textual features.
Vision-and-Language Pretrained Models: A Survey
Pretrained models have produced great success in both Computer Vision (CV) and Natural Language Processing (NLP).
