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Closed captioning

About: Closed captioning is a research topic. Over the lifetime, 3011 publications have been published within this topic receiving 64494 citations. The topic is also known as: CC.


Papers
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Journal ArticleDOI
TL;DR: The results of these surveys confirm that bilingual subtitles are perceived as useful in the different dimensions of the incidental vocabulary learning process (form, meaning, use) and are also helpful when applied to the educational domain (deliberate learning).
Abstract: This paper introduces the concept of bilingual subtitles, a kind of captioning in which a pair of subtitles (in the mother tongue, L1, and second language, L2) is shown at the same time on the screen. The aim of dual subtitles is to help the final user in different learning processes, due to the fact that several capacities (listening, reading, and matching) are exercised at the same time by the learner while watching dual-captioned media. The contribution of this paper is threefold. First, it presents DualSub, an open source desktop tool aimed to create bilingual subtitles. Second, a descriptive study was designed and executed to evaluate the extent to which bilingual subtitles are perceived by final users in the incidental vocabulary knowledge of a second language. Third, an experimental case study in which dual subtitles were used in the engineering education arena was carried out. The results of these surveys confirm that bilingual subtitles are perceived as useful in the different dimensions of the incidental vocabulary learning process (form, meaning, use) and are also helpful when applied to the educational domain (deliberate learning). © 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:468–479, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21814

19 citations

Proceedings ArticleDOI
17 Aug 2018
TL;DR: In this article, the authors proposed a novel architecture for image captioning with deep reinforcement learning to optimize the captioning tasks, which utilizes two networks called policy network and value network to collaboratively generate the captions of images.
Abstract: Recently it has shown that the policy-gradient methods for reinforcement learning have been utilized to train deep end-to-end systems on natural language processing tasks. What's more, with the complexity of understanding image content and diverse ways of describing image content in natural language, image captioning has been a challenging problem to deal with. To the best of our knowledge, most state-of-the-art methods follow a pattern of sequential model, such as recurrent neural networks (RNN). However, in this paper, we propose a novel architecture for image captioning with deep reinforcement learning to optimize image captioning tasks. We utilize two networks called "policy network" and "value network" to collaboratively generate the captions of images. The experiments are conducted on Microsoft COCO dataset, and the experimental results have verified the effectiveness of the proposed method.

19 citations

Journal ArticleDOI
TL;DR: A new multi-sentence video captioning algorithm is proposed using a content-oriented beam search approach and a multi-stage refining method to remove structurally wrong sentences as well as sentences that are less related to the semantic content of the video.
Abstract: With the increasing growth of video data, especially in cyberspace, video captioning or the representation of video data in the form of natural language has been receiving an increasing amount of interest in several applications like video retrieval, action recognition, and video understanding, to name a few In recent years, deep neural networks have been successfully applied for the task of video captioning However, most existing methods describe a video clip using only one sentence that may not correctly cover the semantic content of the video clip In this paper, a new multi-sentence video captioning algorithm is proposed using a content-oriented beam search approach and a multi-stage refining method We use a new content-oriented beam search algorithm to update the probabilities of words generated by the trained deep networks The proposed beam search algorithm leverages the high-level semantic information of an input video using an object detector and the structural dictionary of sentences We also use a multi-stage refining approach to remove structurally wrong sentences as well as sentences that are less related to the semantic content of the video To this intent, a new two-branch deep neural network is proposed to measure the relevance score between a sentence and a video We evaluated the performance of the proposed method with two popular video captioning databases and compared the results with the results of some state-of-the-art approaches The experiments showed the superior performance of the proposed algorithm For instance, in the MSVD database, the proposed method shows an enhancement of 6% for the best-1 sentences in comparison to the best state-of-the-art alternative

19 citations

Proceedings ArticleDOI
01 Jun 2021
TL;DR: In this article, the auxiliary network is provided with the generated caption and one of those two images, and the primary network tries to pick the second image among a set of candidates.
Abstract: We tackle the challenging task of image change captioning. The goal is to describe the subtle difference between two very similar images by generating a sentence caption. While the recent methods mainly focus on proposing new model architectures for this problem, we instead focus on an alternative training scheme. Inspired by the success of multi-task learning, we formulate a training scheme that uses an auxiliary task to improve the training of the change captioning network. We argue that the task of composed query image retrieval is a natural choice as the auxiliary task. Given two almost similar images as the input, the primary network generates a caption describing the fine change between those two images. Next, the auxiliary network is provided with the generated caption and one of those two images. It then tries to pick the second image among a set of candidates. This forces the primary network to generate detailed and precise captions via having an extra supervision loss by the auxiliary network. Furthermore, we propose a new scheme for selecting a negative set of candidates for the retrieval task that can effectively improve the performance. We show that the proposed training strategy performs well on the task of change captioning on benchmark datasets.

19 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023536
20221,030
2021504
2020530
2019448
2018334