Topic
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 published on a yearly basis
Papers
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TL;DR: A double attention model is proposed which combines sentence-level attention model with word- level attention model to generate more accurate captions and outperforms many state-of-the-art image captioning approaches in various evaluation metrics.
23 citations
01 Jun 2021
TL;DR: Wang et al. as mentioned in this paper proposed a video paragraph captioning model which eschews the problematic event detection stage and directly generates paragraphs for untrimmed videos, which progressively exposes new video features and suppresses over-accessed video contents.
Abstract: Video paragraph captioning aims to describe multiple events in untrimmed videos with descriptive paragraphs. Existing approaches mainly solve the problem in two steps: event detection and then event captioning. Such two-step manner makes the quality of generated paragraphs highly dependent on the accuracy of event proposal detection which is already a challenging task. In this paper, we propose a paragraph captioning model which eschews the problematic event detection stage and directly generates paragraphs for untrimmed videos. To describe coherent and diverse events, we propose to enhance the conventional temporal attention with dynamic video memories, which progressively exposes new video features and suppresses over-accessed video contents to control visual focuses of the model. In addition, a diversity-driven training strategy is proposed to improve diversity of paragraph on the language perspective. Considering that untrimmed videos generally contain massive but redundant frames, we further augment the video encoder with keyframe awareness to improve efficiency. Experimental results on the ActivityNet and Charades datasets show that our proposed model significantly outperforms the state-of-the-art performance on both accuracy and diversity metrics without using any event boundary annotations. Code will be released at https://github.com/syuqings/video-paragraph.
23 citations
15 Jan 2015
TL;DR: The rationale and outcomes of ClipFlair, a European-funded project aimed at countering the factors that discourage Foreign Language Learning by providing a motivating, easily accessible online platform to learn a foreign language through revoicing and captioning, are presented.
Abstract: The purpose of this paper is to present the rationale and outcomes of ClipFlair, a European-funded project aimed at countering the factors that discourage Foreign Language Learning (FLL) by providing a motivating, easily accessible online platform to learn a foreign language through revoicing (e.g. dubbing) and captioning (e.g. subtitling). This paper will reflect on what has been achieved throughout the project and the challenges encountered along the way, in order to share our experience and inspire other FLL tutors in secondary and tertiary education. The focus is on the main outputs of the project: a) ClipFlair Studio, an online platform where users (both tutors and learners) can create, upload and access revoicing and captioning activities to learn a foreign language; b) ClipFlair Gallery, a library of resources containing over 350 activities to learn the 15 languages targeted in the project; and c) ClipFlair Social, an online community where learners, teachers and activity authors can share information.
23 citations
Patent•
26 Mar 2014
TL;DR: In this paper, the closed captioning data is associated with a video on demand asset and represented in a first format and then converted to a second format during a single video-on-demand streaming session.
Abstract: A method includes receiving closed captioning data at a computing device. The closed captioning data is associated with a video on demand asset and is represented in a first format. The method also includes, during a single video on demand streaming session, converting the closed captioning data from the first format to a platform-independent format and converting the closed captioning data from the platform-independent format to a second format. The method further includes transmitting, during the single video on demand streaming session, the closed captioning data in the second format to a destination device.
23 citations
01 Mar 2020
TL;DR: This work investigates the problem of figure caption generation where the goal is to automatically generate a natural language description for a given figure, and introduces a dataset FigCAP and proposes novel attention mechanism to solve the exposure bias issue.
Abstract: Figures, such as line plots, pie charts, bar charts, are widely used to convey important information in a concise format. In this work, we investigate the problem of figure caption generation where the goal is to automatically generate a natural language description for a given figure. While natural image captioning has been studied extensively, figure captioning has received relatively little attention and remains a challenging problem. A successful solution to this task has many potential applications, such as: 1) automatic parsing large amount of figures in PDF document; 2) improving user experience by allowing figure content to be accessible to those with visual impairment. To solve this problem, we introduce a dataset FigCAP and propose novel attention mechanism. In order to solve the exposure bias issue, we further train the captioning model with sequence-level policy based on reinforcement learning, which directly optimizes evaluation metrics. Extensive experiments show that the proposed method outperforms the baselines, thus demonstrating a significant potential for automatic generating captions for figures.
23 citations