3,212 research outputs found
VirtualHome: Simulating Household Activities via Programs
In this paper, we are interested in modeling complex activities that occur in
a typical household. We propose to use programs, i.e., sequences of atomic
actions and interactions, as a high level representation of complex tasks.
Programs are interesting because they provide a non-ambiguous representation of
a task, and allow agents to execute them. However, nowadays, there is no
database providing this type of information. Towards this goal, we first
crowd-source programs for a variety of activities that happen in people's
homes, via a game-like interface used for teaching kids how to code. Using the
collected dataset, we show how we can learn to extract programs directly from
natural language descriptions or from videos. We then implement the most common
atomic (inter)actions in the Unity3D game engine, and use our programs to
"drive" an artificial agent to execute tasks in a simulated household
environment. Our VirtualHome simulator allows us to create a large activity
video dataset with rich ground-truth, enabling training and testing of video
understanding models. We further showcase examples of our agent performing
tasks in our VirtualHome based on language descriptions.Comment: CVPR 2018 (Oral
Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?
Prior research has shown how âcontent preview toolsâ improve
speed and accuracy of user relevance judgements across different information retrieval tasks. This paper describes a novel user interface tool, the Content Flow Bar, designed to allow users to quickly identify relevant fragments within informational videos to facilitate browsing, through a cognitively augmented form of navigation. It achieves this by providing semantic âsnippetsâ that enable the user to rapidly scan through video content. The tool provides visuallyappealing pop-ups that appear in a time series bar at the bottom of each video, allowing to see in advance and at a glance how topics evolve in the content. We conducted a user study to evaluate how the tool changes the users search experience in video retrieval, as well as how it supports exploration and information seeking. The user questionnaire revealed that participants found the Content Flow Bar helpful and enjoyable for finding relevant information in videos. The interaction logs of the user study, where participants interacted with the tool for completing two informational tasks, showed that it holds promise for enhancing discoverability of content both across and within videos. This discovered potential could leverage a new generation of navigation tools in search and information retrieval
A Web video retrieval method using hierarchical structure of Web video groups
In this paper, we propose a Web video retrieval method that uses hierarchical structure of Web video groups. Existing retrieval systems require users to input suitable queries that identify the desired contents in order to accurately retrieve Web videos; however, the proposed method enables retrieval of the desired Web videos even if users cannot input the suitable queries. Specifically, we first select representative Web videos from a target video dataset by using link relationships between Web videos obtained via metadata ârelated videosâ and heterogeneous video features. Furthermore, by using the representative Web videos, we construct a network whose nodes and edges respectively correspond to Web videos and links between these Web videos. Then Web video groups, i.e., Web video sets with similar topics are hierarchically extracted based on strongly connected components, edge betweenness and modularity. By exhibiting the obtained hierarchical structure of Web video groups, users can easily grasp the overview of many Web videos. Consequently, even if users cannot write suitable queries that identify the desired contents, it becomes feasible to accurately retrieve the desired Web videos by selecting Web video groups according to the hierarchical structure. Experimental results on actual Web videos verify the effectiveness of our method
Video Augmentation in Education: in-context support for learners through prerequisite graphs
The field of education is experiencing a massive digitisation process that has been ongoing for the past decade. The role played by distance learning and Video-Based Learning, which is even more reinforced by the pandemic crisis, has become an established reality. However, the typical features of video consumption, such as sequential viewing and viewing time proportional to duration, often
lead to sub-optimal conditions for the use of video lessons in the process of acquisition, retrieval and consolidation of
learning contents.
Video augmentation can prove to be an effective support to learners, allowing a more flexible exploration of contents, a better understanding of concepts and relationships between concepts and an optimization of time required for video consumption at different stages of the learning process.
This thesis focuses therefore on the study of
methods for: 1) enhancing video capabilities through video augmentation features; 2) extracting concept and relationships from video materials; 3) developing intelligent user interfaces based on the knowledge extracted.
The main research goal is to understand to what extent video augmentation can improve the learning experience.
This research goal inspired the design of EDURELL Framework, within which two applications were developed to enable the testing of augmented methods and their provision. The novelty of this work lies in using the knowledge within the video, without exploiting external materials, to exploit its educational potential. The enhancement of the user interface takes place through various support features among which in particular a map that progressively highlights the prerequisite relationships between the concepts as they are explained, i.e., following the advancement of the video.
The proposed approach has been designed following a user-centered iterative approach and the results in terms of effect and impact on video comprehension and learning experience make a contribution to the research in this field
A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness
People increasingly use videos on the Web as a source for learning. To
support this way of learning, researchers and developers are continuously
developing tools, proposing guidelines, analyzing data, and conducting
experiments. However, it is still not clear what characteristics a video should
have to be an effective learning medium. In this paper, we present a
comprehensive review of 257 articles on video-based learning for the period
from 2016 to 2021. One of the aims of the review is to identify the video
characteristics that have been explored by previous work. Based on our
analysis, we suggest a taxonomy which organizes the video characteristics and
contextual aspects into eight categories: (1) audio features, (2) visual
features, (3) textual features, (4) instructor behavior, (5) learners
activities, (6) interactive features (quizzes, etc.), (7) production style, and
(8) instructional design. Also, we identify four representative research
directions: (1) proposals of tools to support video-based learning, (2) studies
with controlled experiments, (3) data analysis studies, and (4) proposals of
design guidelines for learning videos. We find that the most explored
characteristics are textual features followed by visual features, learner
activities, and interactive features. Text of transcripts, video frames, and
images (figures and illustrations) are most frequently used by tools that
support learning through videos. The learner activity is heavily explored
through log files in data analysis studies, and interactive features have been
frequently scrutinized in controlled experiments. We complement our review by
contrasting research findings that investigate the impact of video
characteristics on the learning effectiveness, report on tasks and technologies
used to develop tools that support learning, and summarize trends of design
guidelines to produce learning video
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