In order to understand and fully comprehend a subtitle, two parameters within the linguistic code of audiovisual texts are key in the processing of the subtitle itself, namely, vocabulary and syntax. Through a descriptive and experimental study, the present article explores the transfer of the linguistic code of audiovisual texts in subtitling for deaf and hard-of-hearing children in three Span...

When describing an image, people can rapidly extract the topic from image and find main object, generating sentences that match idea of image. However, most scene graph generation methods do not emphasise importance Consequently, captions generated by graph-based captioning models cannot reflect in then expressing central In this paper, we propose a method for based on graphs (TSG). Firstly, st...

This paper considers a video caption generating network referred to as Semantic Grouping Network (SGN) that attempts (1) group frames with discriminating word phrases of partially decoded and then (2) decode those semantically aligned groups in predicting the next word. As consecutive are not likely provide unique information, prior methods have focused on discarding or merging repetitive infor...

1.1.1 Video Captioning Datasets YouTube2Text or MSVD The Microsoft Research Video Description Corpus (MSVD) or YouTube2Text (Chen and Dolan, 2011) is used for our primary video captioning experiments. It has 1970 YouTube videos in the wild with many diverse captions in multiple languages for each video. Caption annotations to these videos are collected using Amazon Mechanical Turk (AMT). All ou...

The attention mechanism is an important part of the neural machine translation (NMT) where it was reported to produce richer source representation compared to fixed-length encoding sequence-to-sequence models. Recently, the effectiveness of attention has also been explored in the context of image captioning. In this work, we assess the feasibility of a multimodal attention mechanism that simult...

Image captioning, a popular topic in computer vision, has achieved substantial progress in recent years. However, the distinctiveness of natural descriptions is often overlooked in previous work. It is closely related to the quality of captions, as distinctive captions are more likely to describe images with their unique aspects. In this work, we propose a new learning method, Contrastive Learn...

Visual captioning aims to generate textual descriptions given images or videos. Traditionally, image models are trained on human annotated datasets such as Flickr30k and MS-COCO, which limited in size diversity. This limitation hinders the generalization capabilities of these while also rendering them liable making mistakes. Language can, however, be vast amounts freely available unlabelled dat...

Image captioning is a problem of viewing images and describing in language. This an important that can be solved by understanding the image, combining two fields image processing natural language into one. The purpose research so far has been to create general explanatory captions learning data. However, various environments reality must considered for practical use, as well descriptions suit u...