Video summarization technologies aim to create a concise and complete synopsis by selecting the most informative parts of the video content. Several approaches have been developed over the last couple of decades, and the current state of the art is represented by methods that rely on modern deep neural network architectures. 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. After presenting the motivation behind the development of technologies for video summarization, we formulate the video summarization task and discuss the main characteristics of a typical deep-learning-based analysis pipeline. Then, we suggest a taxonomy of the existing algorithms and provide a systematic review of the relevant literature that shows the evolution of the deep-learning-based video summarization technologies and leads to suggestions for future developments. We then report on protocols for the objective evaluation of video summarization algorithms, and we compare the performance of several deep-learning-based approaches. Based on the outcomes of these comparisons, as well as some documented considerations about the amount of annotated data and the suitability of evaluation protocols, we indicate potential future research directions.

Video Summarization Using Deep Neural Networks: A Survey

In video understanding, action spotting consists in temporally localizing human-induced events annotated with single timestamps. In this paper, we propose a novel loss function that specifically considers the temporal context naturally present around each action, rather than focusing on the single annotated frame to spot. We benchmark our loss on a large dataset of soccer videos, SoccerNet, and achieve an improvement of 12.8% over the baseline. We show the generalization capability of our loss for generic activity proposals and detection on ActivityNet, by spotting the beginning and the end of each activity. Furthermore, we provide an extended ablation study and display challenging cases for action spotting in soccer videos. Finally, we qualitatively illustrate how our loss induces a precise temporal understanding of actions and show how such semantic knowledge can be used for automatic highlights generation.

/pdf/a-context-aware-loss-function-for-action-spotting-in-soccer-22pvq1yvm9.pdf

A Context-Aware Loss Function for Action Spotting in Soccer Videos

Soccer broadcast video understanding has been drawing a lot of attention in recent years within data scientists and industrial companies. This is mainly due to the lucrative potential unlocked by effective deep learning techniques developed in the field of computer vision. In this work, we focus on the topic of camera calibration and on its current limitations for the scientific community. More precisely, we tackle the absence of a large-scale calibration dataset and of a public calibration network trained on such a dataset. Specifically, we distill a powerful commercial calibration tool in a recent neural network architecture on the large-scale SoccerNet dataset, composed of untrimmed broadcast videos of 500 soccer games. We further release our distilled network, and leverage it to provide 3 ways of representing the calibration results along with player localization. Finally, we exploit those representations within the current best architecture for the action spotting task of SoccerNetv2, and achieve new state-of-the-art performances.

/pdf/camera-calibration-and-player-localization-in-soccernet-v2-1j49d8vyv2.pdf

Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting

Soccer videos are a rich playground for computer vision, involving many elements, such as players, lines, and specific objects. Hence, to capture the richness of this sport and allow for fine automated analyses, we release SoccerNet-v3, a major extension of the SoccerNet dataset, providing a wide variety of spatial annotations and cross-view correspondences. SoccerNet's broadcast videos contain replays of important actions, allowing us to retrieve a same action from different viewpoints. We annotate those live and replay action frames showing same moments with exhaustive local information. Specifically, we label lines, goal parts, players, referees, teams, salient objects, jersey numbers, and we establish player correspondences between the views. This yields 1,324,732 annotations on 33,986 soccer images, making SoccerNet-v3 the largest dataset for multi-view soccer analysis. Derived tasks may benefit from these annotations, like camera calibration, player localization, team discrimination and multi-view re-identification, which can further sustain practical applications in augmented reality and soccer analytics. Finally, we provide Python codes to easily download our data and access our annotations. 

/pdf/scaling-up-soccernet-with-multi-view-spatial-localization-24yih10h.pdf

Scaling up SoccerNet with multi-view spatial localization and re-identification

Traditional video summarization methods generate fixed video representations regardless of user interest. Therefore such methods limit users' expectations in content search and exploration scenarios. Multi-modal video summarization is one of the methods utilized to address this problem. When multi-modal video summarization is used to help video exploration, a text-based query is considered as one of the main drivers of video summary generation, as it is user-defined. Thus, encoding both the text-based query and the video effectively is important for the task of multi-modal video summarization. In this work, a new method is proposed that uses a specialized attention network and contextualized word representations to tackle this task. The proposed model consists of a contextualized video summary controller, multi-modal attention mechanisms, an interactive attention network, and a video summary generator. Based on the evaluation of the existing multi-modal video summarization benchmark, experimental results show that the proposed model is effective with the increase of +5.88% in accuracy and +4.06% increase of F1-score, compared with the state-of-the-art method. https://github.com/Jhhuangkay/GPT2MVS-Generative-Pre-trained-Transformer-2-for-Multi-modal-Video-Summarization.

pdf/gpt2mvs-generative-pre-trained-transformer-2-for-multi-modal-2ucbls8zpv.pdf

GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization

The massive growth of sports videos, specially in soccer, has resulted in a need for the automatic generation of summaries, where the objective is not only to show the most important actions of the match but also to elicit as much emotion as the ones bring upon by human editors. State-of-the-art methods on video summarization mostly rely on video processing, however this is not an optimal approach for long videos such as soccer matches. In this paper we propose a multimodal approach to automatically generate summaries of soccer match videos that consider both event and audio features. The event features get a shorter and better representation of the match, and the audio helps detect the excitement generated by the game. Our method consists of three consecutive stages: Proposals, Summarization and Content Refinement. The first one generates summary proposals, using Multiple Instance Learning to deal with the similarity between the events inside the summary and the rest of the match. The Summarization stage uses event and audio features as input of a hierarchical Recurrent Neural Network to decide which proposals should indeed be in the summary. And the last stage, takes advantage of the visual content to create the final summary. The results show that our approach outperforms by a large margin not only the video processing methods but also methods that use event and audio features.

A Deep Architecture for Multimodal Summarization of Soccer Games

Vocational High Schools are educational institution aiming to prepare the ready-to-employ graduates by prioritizing their skill and expertise according to their areas of expertise (Azri, Irwan, & Nurul, 2020; Mukhlason, Winanti, & Yundra, 2020). Vocational High School is expected to play an important role in holistic human development and community development Indonesia entirely and reduce the number of early school leaver (Slamet, 2016; Diwangkoro & Soenarto, 2020; Farrugia & Martina, 2021). While the growth of employment in the industrial sector is growing slowly, the graduates of Vocational High Schools are increasing but the rate of joblessness of the Vocational Highs Schools graduates has become a critical issue in Indonesia (Erlinawati, 2020; Wijaya & Utami, 2021; Hanifah & Suharyanto, 2021). As a result, these Vocational High Schools graduates are not accommodated in the industrial sector and they become job seekers or open unemployment (Dewi, 2017; Yunikawati, Prayitno, Purboyo, Istiqomah, & Puspasari, 2018; Azman, Ambiyar, Simatupang, Karudin, & Dakhi, 2020). The Central Statistics Agency (BPS) reported that in August 2021, the majority of unemployed people in Indonesia were dominated to 11.13% or 9.77 million by the graduates from Vocational High Schools (BPS, 2021). Most of the Vocational High Schools graduates intend to work immediately, but are not employed in the industry or business sector. This is due to the increase of Vocational High Schools graduates who are mismatched by job opportunities.


Keywords: Dual Certification; Vocational High School; Employability

Vocational High School Prospective Graduates' Employability via Dual Vocational Certification (DVC)

Certification Competency test is one of the requirements of VHS graduates and the gap between theory and phenomenon in this test evaluation is rarely carried out. The objective of this study is to evaluate the implementation of Certification Competency Test of VHS. A thorough evaluative method with Discrepancy Evaluation Model (DEM) was implemented through observation, and document study. Then, a questionnaire was developed to collect the data from 13 VHS as the population and sample. The data analysed descriptively fell into some categories as the finding of this research such as design, installation, process, product shared very good achievement with score 31.53 (100%), 57.4 (88.51%), 39.64 (87.52%), and 43.87 (89.22%) respectively.  The overall score denoted very good category with average gained score 172.44 (91.32%). In conclusion, the certification competency exam requires more maximum improvement despite the recent achievement. Good cooperation is needed from all parties, such as the provincial level national exam implementers, schools, partner institutions, supervisors, parents/community and students so as to fulfill the gap so that the certification competency test results will be even better.

/pdf/discrepancy-evaluation-model-on-certification-competency-2y5o466o.pdf

Discrepancy Evaluation Model on Certification Competency Test Implementation of Vocational High School


 
 
 
Learners are the main and most important resource in the process of formal education. The existence of students is not just meeting their needs but is part of the quality of educational institutions. This means that quality student management is needed for these educational institutions so that students can grow and develop according to their physical condition and intellectual intelligence. Most students face many difficulties in solving problems given by the teacher, even though the information is clear and complete, while the teacher faced difficulties in educating participants to find ways to solve problems well. To be able to solve the problem properly, several things are necessarily considered including time spent on problem-solving, lesson planning, and learning resources needed. Thus expected application of the Somatic Auditory Visualization Intellectual learning model (SAVI). combined with teaching aids will further add to the variety of learning models that are more interesting, increase the activity and cooperation of students and improve students' problem-solving abilities.  
 
 
 


The Influence of Using Learning Modules on Students' Cognitive Learning Outcomes in Educational Management

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Papers

A Deep Architecture for Multimodal Summarization of Soccer Games

Vocational High School Prospective Graduates' Employability via Dual Vocational Certification (DVC)

Discrepancy Evaluation Model on Certification Competency Test Implementation of Vocational High School

The Influence of Using Learning Modules on Students' Cognitive Learning Outcomes in Educational Management