Weakly Supervised Video Moment Retrieval From Text Queries

Chowdhury, Mithun Niluthpol; Paul, Sujoy; Amit, K Roy-Chowdhury

doi:10.48550/arxiv.1904.03282

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model inputs both image and video data and a test description of the events taking place. Three application tasks are handled (Action Understanding [11], Video-Language Alignment [12] and Video Open Understanding [5]) and used a set of corresponding datasets to work with each of them (Kinetics [13], ActivityNet [14], MSR-VTT [15], DiDeMo [16], UFC101-HMDB51 [17], Ego4d [18], etc. ).…”

Section: Fig 7 Bar Chart Of All Three Models Performancementioning

confidence: 99%

Human action analysis models in artificial intelligence based proctoring systems and dataset for them

Antoshchuk¹,

Breskina²

2023

AAIT

View full text Add to dashboard Cite

This paper describes the approach for building a specialized model for human action analysis in AI-based proctoring systemsand proposes a prototype of dataset which contains data specific to the application area. Boosted development of machine learningtechnologies, the availability of devicesand the access to the Internet are skyrocketing the development of the field of distance learning. And in parallel with distance learning systemstheAI-based proctoring systems, that provide the functional analysis of student work by imitating the teacher's assessment, are developing as well. However, despite the development of image processing and machine learning technology, the functionality of modern proctoring systems is still at a primitive level. Within the image processing functionality, they focus entirely on tracking students' faces and do not track postures and actions. At the same time, assessment of physical activity is necessary not only as part of the learning process, but also to keep students healthy according to regulatory requirements, as they spend the entire duration of learning process in front of computers or other devices during the distance learning. In existing implementations, this process falls entirely on the shoulders of teachers or even the studentsthemselves, who work through the lesson materials or tests on their own. Teachers, at the same time, have toeither establish contact through video communication systems and social media (TikTok, Instagram) and/or analyse videos of students doing certain physical activities in ordertoorganise physical activities evaluation. The lack of such functionality in AI-based proctoring systemsslows down the learning process and potentially harms students' health in the long run. This paper presents additional functionality requirements forAI-based proctoring systemsincluding human action analysis functionality to assess physical activity and to monitor hygiene rules for working with computers during the educational process. For this purpose, afoundation modelcalledInternVideo was used for processing and analysis of student's actions. Based on it, the approach for building a specialized model for student action analysis was proposed. It includes two modes of student activity evaluation during the distance learning process: static and dynamic. The static mode (aka working phase) analyses and evaluates the student's behavior during the learning and examination process, where physical activity is not the main component of learning. The dynamic mode (aka physical education mode) analyses and assesses the student who purposefully performs physical activity (physical education lesson, exercises for children during the lesson, etc.). A prototype dataset designed specifically for this application area has also been proposed.

show abstract