A multi-purpose automatic editing system based on lecture semantics for remote education

• URL: http://arxiv.org/abs/2411.04859v1
• Date: Thu, 07 Nov 2024 16:49:25 GMT
• Title: A multi-purpose automatic editing system based on lecture semantics for remote education
• Authors: Panwen Hu, Rui Huang,
• Abstract summary: This paper proposes an automatic multi-camera directing/editing system based on the lecture semantics. Our system directs the views by semantically analyzing the class events while following the professional directing rules.
• Score: 6.6826236187037305
• License:
• Abstract: Remote teaching has become popular recently due to its convenience and safety, especially under extreme circumstances like a pandemic. However, online students usually have a poor experience since the information acquired from the views provided by the broadcast platforms is limited. One potential solution is to show more camera views simultaneously, but it is technically challenging and distracting for the viewers. Therefore, an automatic multi-camera directing/editing system, which aims at selecting the most concerned view at each time instance to guide the attention of online students, is in urgent demand. However, existing systems mostly make simple assumptions and focus on tracking the position of the speaker instead of the real lecture semantics, and therefore have limited capacities to deliver optimal information flow. To this end, this paper proposes an automatic multi-purpose editing system based on the lecture semantics, which can both direct the multiple video streams for real-time broadcasting and edit the optimal video offline for review purposes. Our system directs the views by semantically analyzing the class events while following the professional directing rules, mimicking a human director to capture the regions of interest from the viewpoint of the onsite students. We conduct both qualitative and quantitative analyses to verify the effectiveness of the proposed system and its components.

Related papers

• Multimodality in Online Education: A Comparative Study [2.0472158451829827]
  Current systems consider only a single cue with a lack of focus in the educational domain. This paper highlights the need for a multimodal approach to affect recognition and its deployment in the online classroom. It compares the various machine learning models available for each cue and provides the most suitable approach.
  arXiv  Detail & Related papers  (2023-12-10T07:12:15Z)
• Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation [74.51546366251753]
  Video topic segmentation unveils the coarse-grained semantic structure underlying videos. We introduce a multi-modal video topic segmenter that utilizes both video transcripts and frames. Our proposed solution significantly surpasses baseline methods in terms of both accuracy and transferability.
  arXiv  Detail & Related papers  (2023-11-30T21:59:05Z)
• Learning to Select Camera Views: Efficient Multiview Understanding at Few Glances [59.34619548026885]
  We propose a view selection approach that analyzes the target object or scenario from given views and selects the next best view for processing. Our approach features a reinforcement learning based camera selection module, MVSelect, that not only selects views but also facilitates joint training with the task network.
  arXiv  Detail & Related papers  (2023-03-10T18:59:10Z)
• AutoLV: Automatic Lecture Video Generator [16.73368874008744]
  We propose an end-to-end lecture video generation system. It can generate realistic and complete lecture videos directly from annotated slides, instructor's reference voice and instructor's reference portrait video.
  arXiv  Detail & Related papers  (2022-09-19T07:00:14Z)
• Smart Director: An Event-Driven Directing System for Live Broadcasting [110.30675947733167]
  Smart Director aims at mimicking the typical human-in-the-loop broadcasting process to automatically create near-professional broadcasting programs in real-time. Our system is the first end-to-end automated directing system for multi-camera sports broadcasting.
  arXiv  Detail & Related papers  (2022-01-11T16:14:41Z)
• Weakly Supervised Visual-Auditory Saliency Detection with Multigranularity Perception [46.84865384147999]
  Deep learning-based visualaudio fixation prediction is still in its infancy. It would be neither efficient nor necessary to recollect real fixations under the same visual-audio circumstances. This paper promotes a novel approach in a weakly supervised manner to alleviate the demand of large-scale training sets for visual-audio model training.
  arXiv  Detail & Related papers  (2021-12-27T14:13:30Z)
• A Clustering-Based Method for Automatic Educational Video Recommendation Using Deep Face-Features of Lecturers [0.0]
  This paper presents a method for generating educational video recommendation using deep face-features of lecturers without identifying them. We use an unsupervised face clustering mechanism to create relations among the videos based on the lecturer's presence. We rank these recommended videos based on the amount of time the referenced lecturers were present.
  arXiv  Detail & Related papers  (2020-10-09T16:53:16Z)
• Weakly-Supervised Multi-Level Attentional Reconstruction Network for Grounding Textual Queries in Videos [73.4504252917816]
  The task of temporally grounding textual queries in videos is to localize one video segment that semantically corresponds to the given query. Most of the existing approaches rely on segment-sentence pairs (temporal annotations) for training, which are usually unavailable in real-world scenarios. We present an effective weakly-supervised model, named as Multi-Level Attentional Reconstruction Network (MARN), which only relies on video-sentence pairs during the training stage.
  arXiv  Detail & Related papers  (2020-03-16T07:01:01Z)
• Visually Guided Self Supervised Learning of Speech Representations [62.23736312957182]
  We propose a framework for learning audio representations guided by the visual modality in the context of audiovisual speech. We employ a generative audio-to-video training scheme in which we animate a still image corresponding to a given audio clip and optimize the generated video to be as close as possible to the real video of the speech segment. We achieve state of the art results for emotion recognition and competitive results for speech recognition.
  arXiv  Detail & Related papers  (2020-01-13T14:53:22Z)

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.