Interactive Summarizing -- Automatic Slide Localization Technology as Generative Learning Tool

• URL: http://arxiv.org/abs/2002.11203v1
• Date: Tue, 25 Feb 2020 22:22:49 GMT
• Title: Interactive Summarizing -- Automatic Slide Localization Technology as Generative Learning Tool
• Authors: Lili Yan and Kai Li
• Abstract summary: Video summarization is an effective technology applied to enhance learners' summarizing experience in a video lecture. An interactive summarizing model is designed to explain how learners are engaged in the video lecture learning process supported by convolutional neural network.
• Score: 10.81386784858998
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Making a summary is a common learning strategy in lecture learning. It is an effective way for learners to engage in both traditional and video lectures. Video summarization is an effective technology applied to enhance learners' summarizing experience in a video lecture. In this article, we propose to apply cutting-edge automatic slide localization technology to lecture video learning experience. An interactive summarizing model is designed to explain how learners are engaged in the video lecture learning process supported by convolutional neural network and the possibility of related learning analytics.

Related papers

• Video Summarization Techniques: A Comprehensive Review [1.6381055567716192]
  The paper explores the various approaches and methods created for video summarizing, emphasizing both abstractive and extractive strategies. The process of extractive summarization involves the identification of key frames or segments from the source video, utilizing methods such as shot boundary recognition, and clustering. On the other hand, abstractive summarization creates new content by getting the essential content from the video, using machine learning models like deep neural networks and natural language processing, reinforcement learning, attention mechanisms, generative adversarial networks, and multi-modal learning.
  arXiv  Detail & Related papers  (2024-10-06T11:17:54Z)
• Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination [52.20542825755132]
  We develop Slide2Lecture, a tuning-free and knowledge-regulated intelligent tutoring system. It can effectively convert an input lecture slide into a structured teaching agenda consisting of a set of heterogeneous teaching actions. For teachers and developers, Slide2Lecture enables customization to cater to personalized demands.
  arXiv  Detail & Related papers  (2024-09-11T16:03:09Z)
• Intelligent Interface: Enhancing Lecture Engagement with Didactic Activity Summaries [0.054204929130712134]
  The prototype utilizes machine learning-based techniques to recognise selected didactic and behavioural teachers' features within lecture video recordings. The system offers flexibility for (future) integration of new/additional machine-learning models and software modules for image and video analysis.
  arXiv  Detail & Related papers  (2024-06-20T12:45:23Z)
• FastPerson: Enhancing Video Learning through Effective Video Summarization that Preserves Linguistic and Visual Contexts [23.6178079869457]
  We propose FastPerson, a video summarization approach that considers both the visual and auditory information in lecture videos. FastPerson creates summary videos by utilizing audio transcriptions along with on-screen images and text. It reduces viewing time by 53% at the same level of comprehension as that when using traditional video playback methods.
  arXiv  Detail & Related papers  (2024-03-26T14:16:56Z)
• Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems [46.760568562468606]
  Few-shot meta-learning optimize learning algorithms that can efficiently adapt to new tasks quickly. This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications.
  arXiv  Detail & Related papers  (2022-10-03T17:15:36Z)
• Multimodal Lecture Presentations Dataset: Understanding Multimodality in Educational Slides [57.86931911522967]
  We test the capabilities of machine learning models in multimodal understanding of educational content. Our dataset contains aligned slides and spoken language, for 180+ hours of video and 9000+ slides, with 10 lecturers from various subjects. We introduce PolyViLT, a multimodal transformer trained with a multi-instance learning loss that is more effective than current approaches.
  arXiv  Detail & Related papers  (2022-08-17T05:30:18Z)
• Video-Text Pre-training with Learned Regions [59.30893505895156]
  Video-Text pre-training aims at learning transferable representations from large-scale video-text pairs. We propose a module for videotext-learning, RegionLearner, which can take into account the structure of objects during pre-training on large-scale video-text pairs.
  arXiv  Detail & Related papers  (2021-12-02T13:06:53Z)
• Video Summarization Using Deep Neural Networks: A Survey [72.98424352264904]
  Video summarization technologies aim to create a concise and complete synopsis by selecting the most informative parts of the video content. This work focuses on the recent advances in the area and provides a comprehensive survey of the existing deep-learning-based methods for generic video summarization.
  arXiv  Detail & Related papers  (2021-01-15T11:41:29Z)
• Learning Grammar of Complex Activities via Deep Neural Networks [0.0]
  This report provides a theoretical insight into deep neural networks for video learning, under label constraints. I build upon previous work in video learning for computer vision, make observations on model performance and propose further mechanisms to help improve our observations.
  arXiv  Detail & Related papers  (2021-01-07T21:48:58Z)
• Neuro-Symbolic Representations for Video Captioning: A Case for Leveraging Inductive Biases for Vision and Language [148.0843278195794]
  We propose a new model architecture for learning multi-modal neuro-symbolic representations for video captioning. Our approach uses a dictionary learning-based method of learning relations between videos and their paired text descriptions.
  arXiv  Detail & Related papers  (2020-11-18T20:21:19Z)
• Learning Video Representations from Textual Web Supervision [97.78883761035557]
  We propose to use text as a method for learning video representations. We collect 70M video clips shared publicly on the Internet and train a model to pair each video with its associated text. We find that this approach is an effective method of pre-training video representations.
  arXiv  Detail & Related papers  (2020-07-29T16:19:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.