A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text. We address this problem by introducing explicit representations for objects and their relations by extracting scene graphs from the images. Utilizing an embedding of this scene graph enables our model to more explicitly reason over objects and their relations during story generation, compared to the global features from an object classifier used in previous work. We apply metrics that account for the diversity of words and phrases of generated stories as well as for reference to narratively-salient image features and show that our approach outperforms previous systems. Our experiments also indicate that our models obtain competitive results on reference-based metrics.
Introduction: Non-carious lesions are caused as a result of tooth surface loss. Several categories of tooth surface loss exist, including erosion, attrition, abrasion and abfraction. Numerous factors, such as bruxism, clenching, disease, dietary considerations, lifestyle choices, improper tooth brushing, abrasive dentrifices, craniofacial complex, iatrogenic dentistry and ageing might contribute to this problem. It can be challenging to identify the cause, but it is feasible by observing the pattern of tooth surface loss on the teeth, and it is essential for treatment planning to avoid failure. Prevention, tooth remineralization and active treatment by repairing the affected teeth are all methods of managing this process. Treatment options include minimally invasive and adhesive dentistry to full mouth rehabilitation, and restoring the lost vertical height. Case Report: A 45-year-old female patient reported to the Department of Conservative Dentistry and Endodontics with a chief complaint of sensitivity in the upper front teeth for the past 2 months. The clinical examination showed abrasion on the buccal surface of teeth 13 and 23 with dentin exposure. And also, abfraction with respect to 14. No signs of mobility or pain on percussion. Conclusion: The steps of problem identification, diagnosis, etiological factor removal or treatment, and, if necessary, restoration, are components of treating non-caries lesions. The restorative treatment must be considered for dentin hypersensitivity and for the re-establishing of dental esthetics.
Medical image classification is one of the most critical problems in the image recognition area. One of the major challenges in this field is the scarcity of labelled training data. Additionally, there is often class imbalance in datasets as some cases are very rare to happen. As a result, accuracy in classification task is normally low. Deep Learning models, in particular, show promising results on image segmentation and classification problems, but they require very large datasets for training. Therefore, there is a need to generate more of synthetic samples from the same distribution. Previous work has shown that feature generation is more efficient and leads to better performance than corresponding image generation [12]. We apply this idea in the Medical Imaging domain. We use transfer learning to train a segmentation model for the small dataset for which goldstandard class annotations are available. We extracted the learnt features and use them to generate synthetic features conditioned on class labels, using Auxiliary Classifier GAN (ACGAN). We test the quality of the generated features in a downstream classification task for brain tumors according to their severity level. Experimental results show a promising result regarding the validity of these generated features and their overall contribution to balancing the data and improving the classification class-wise accuracy.
Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent, diverse, and visually grounded compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and diverse than stories generated with the current state-of-the-art model. Our code, image features, annotations and collected stories are available at https://vwprompt.github.io/.
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