scholarly journals Deep learning for video classification and captioning

2017 ◽  
pp. 3-29 ◽  
Author(s):  
Zuxuan Wu ◽  
Ting Yao ◽  
Yanwei Fu ◽  
Yu-Gang Jiang
Keyword(s):  
Deep Learning ◽  
Video Classification

scholarly journals Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification

2018 ◽  
Vol 20 (11) ◽  
pp. 3137-3147 ◽  
Author(s):  
Yu-Gang Jiang ◽  
Zuxuan Wu ◽  
Jinhui Tang ◽  
Zechao Li ◽  
Xiangyang Xue ◽  
...  
Keyword(s):  
Deep Learning ◽  
Video Classification ◽  
Learning Framework

scholarly journals Deep Learning Approaches to Automated Video Classification of Upper Limb Tension Test

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1579
Author(s):  
Wansuk Choi ◽  
Seoyoon Heo
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
High Performance ◽  
Model Fit ◽  
Fine Tuning ◽  
Learning Approaches ◽  
Video Classification ◽  
Video Clips ◽  
Performance Computing

The purpose of this study was to classify ULTT videos through transfer learning with pre-trained deep learning models and compare the performance of the models. We conducted transfer learning by combining a pre-trained convolution neural network (CNN) model into a Python-produced deep learning process. Videos were processed on YouTube and 103,116 frames converted from video clips were analyzed. In the modeling implementation, the process of importing the required modules, performing the necessary data preprocessing for training, defining the model, compiling, model creation, and model fit were applied in sequence. Comparative models were Xception, InceptionV3, DenseNet201, NASNetMobile, DenseNet121, VGG16, VGG19, and ResNet101, and fine tuning was performed. They were trained in a high-performance computing environment, and validation and loss were measured as comparative indicators of performance. Relatively low validation loss and high validation accuracy were obtained from Xception, InceptionV3, and DenseNet201 models, which is evaluated as an excellent model compared with other models. On the other hand, from VGG16, VGG19, and ResNet101, relatively high validation loss and low validation accuracy were obtained compared with other models. There was a narrow range of difference between the validation accuracy and the validation loss of the Xception, InceptionV3, and DensNet201 models. This study suggests that training applied with transfer learning can classify ULTT videos, and that there is a difference in performance between models.

scholarly journals A Feasibility Test on Preventing PRMDs Based on Deep Learning

2019 ◽  
Vol 33 ◽  
pp. 10005-10006
Author(s):  
So-Hyun Park ◽  
Sun-Young Ihm ◽  
Aziz Nasridinov ◽  
Young-Ho Park
Keyword(s):  
Deep Learning ◽  
Musculoskeletal Disorders ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Video Classification ◽  
Learning Paradigm ◽  
Feasibility Test ◽  
Piano Playing

This study proposes a method to reduce the playing-related musculoskeletal disorders (PRMDs) that often occur among pianists. Specifically, we propose a feasibility test that evaluates several state-of-the-art deep learning algorithms to prevent injuries of pianist. For this, we propose (1) a C3P dataset including various piano playing postures and show (2) the application of four learning algorithms, which demonstrated their superiority in video classification, to the proposed C3P datasets. To our knowledge, this is the first study that attempted to apply the deep learning paradigm to reduce the PRMDs in pianist. The experimental results demonstrated that the classification accuracy is 80% on average, indicating that the proposed hypothesis about the effectiveness of the deep learning algorithms to prevent injuries of pianist is true.

scholarly journals VideoRecognition - Uma proposta de serviço para reconhecimento de elementos de vídeo em larga escala

2018 ◽  
Author(s):  
Antonio J.G. Busson ◽  
Álan L.V. Guedes ◽  
Gabriel N.P. Dos Santos ◽  
Carlos de Salles Soares Neto ◽  
Ruy Luiz Milidiú ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speech Processing ◽  
The State ◽  
Video Classification ◽  
Multimedia Services ◽  
Traditional Use ◽  
Ongoing Research ◽  
It Usage ◽  
Learning Research ◽  
State Of Art

Deep Learning research has allowed significant advancement of various segments of multimedia, especially in tasks related to speech processing, hearing and computational vision. However, some video services are still focused only on the traditional use of media (capture, storage, transmission and presentation). In this paper, we discuss our ongoing research towards a DLaS, i.e. Deep Learning as a Service. This way, we present the state of art in video classification and recognition. Then we propose the VideoRecognition as DLaS to support the tasks such as: image classification and video scenes, object detection and facial recognition. We discuss the usage of the proposed service in the context of the video@RNP repository. Our main contributions consist on dicussussions over the state of art and it usage in nowdays multimedia services.

scholarly journals Deep Learning for Video Classification: A Review

2021 ◽  
Author(s):  
Atiq Rehman ◽  
Samir Brahim Belhaouari
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Classification Task ◽  
Future Research ◽  
Video Classification ◽  
Learning Models ◽  
Learning Methods ◽  
Research Directions ◽  
Concise Review ◽  
State Of Art

<div><div><div><p>Video classification task has gained a significant success in the recent years. Specifically, the topic has gained more attention after the emergence of deep learning models as a successful tool for automatically classifying videos. In recognition to the importance of video classification task and to summarize the success of deep learning models for this task, this paper presents a very comprehensive and concise review on the topic. There are a number of existing reviews and survey papers related to video classification in the scientific literature. However, the existing review papers are either outdated, and therefore, do not include the recent state-of-art works or they have some limitations. In order to provide an updated and concise review, this paper highlights the key findings based on the existing deep learning models. The key findings are also discussed in a way to provide future research directions. This review mainly focuses on the type of network architecture used, the evaluation criteria to measure the success, and the data sets used. To make the review self- contained, the emergence of deep learning methods towards automatic video classification and the state-of-art deep learning methods are well explained and summarized. Moreover, a clear insight of the newly developed deep learning architectures and the traditional approaches is provided, and the critical challenges based on the benchmarks are highlighted for evaluating the technical progress of these methods. The paper also summarizes the benchmark datasets and the performance evaluation matrices for video classification. Based on the compact, complete, and concise review, the paper proposes new research directions to solve the challenging video classification problem.</p></div></div></div>

scholarly journals Video Classification Using Deep Learning

2020 ◽  
pp. 406-413
Author(s):  
Sheshang Degadwala ◽  
Harsh Parekh ◽  
Nirav Ghodadra ◽  
Harsh Chauhan ◽  
Mashkoor Hussaini
Keyword(s):  
Deep Learning ◽  
Human Movement ◽  
Point Of View ◽  
High Dimensional ◽  
Video Classification ◽  
Wide Spread ◽  
Dynamic Scene ◽  
The Mind

Video order has been comprehensively investigated in PC vision in view of its wide spread applications. In any case, it remains a surprising task by virtue of the mind boggling troubles in fruitful segment extraction and successful arrangement with high-dimensional video depictions. Video groupings present uncommon irregularity as a result of monster scope changes, point of view assortment, and camera development, which pose fantastic challenges for both video depictions and characterization. With the phenomenal accomplishment of significant learning, convolutional neural frameworks (CNNs) and their 3-D varieties have been considered in the video territory for an immense grouping of order assignments. Video depictions have accepted a basically noteworthy activity in video examination, which authentically impact a complete execution of video characterization. Both the spatial and brief information should be gotten and encoded for extensive and educational depiction of video progressions. Significant Learning feature level blend plans have exceptional capacity of video depictions for improving the introduction of video grouping. We have driven wide assessment on four going after for video arrangement including human movement affirmation and dynamic scene grouping.

Sign in / Sign up

Export Citation Format

Share Document