Search Results for author:
Found 8 papers, 6 papers with code
Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender
In addition, we propose a visual semantic-based gender score that measures the degree of bias and can be used as a plug-in for any image captioning system.
Visual Semantic Relatedness Dataset for Image Captioning
Modern image captioning system relies heavily on extracting knowledge from images to capture the concept of a static story.
Word to Sentence Visual Semantic Similarity for Caption Generation: Lessons Learned
This paper focuses on enhancing the captions generated by image-caption generation systems.
Belief Revision based Caption Re-ranker with Visual Semantic Information
In this work, we focus on improving the captions generated by image-caption generation systems.
Textual Visual Semantic Dataset for Text Spotting
In this paper, we propose a visual context dataset for Text Spotting in the wild, where the publicly available dataset COCO-text [Veit et al. 2016] has been extended with information about the scene (such as objects and places appearing in the image) to enable researchers to include semantic relations between texts and scene in their Text Spotting systems, and to offer a common framework for such approaches.
Semantic Relatedness Based Re-ranker for Text Spotting
We present a scenario where semantic similarity is not enough, and we devise a neural approach to learn semantic relatedness.
Visual Re-ranking with Natural Language Understanding for Text Spotting
We propose a post-processing approach to improve scene text recognition accuracy by using occurrence probabilities of words (unigram language model), and the semantic correlation between scene and text.
Visual Semantic Re-ranker for Text Spotting
In this paper, we propose a post-processing approach to improve the accuracy of text spotting by using the semantic relation between the text and the scene.
