Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userHuman Action Recognition from video data
التعرف على النشاط البشري من مقطع فيديو
954
18 47
0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نهدف في هذه الأطروحة إلى التعرف على النشاط البشري من مقطع فيديو. نبدأ بدراسة مرجعية تشمل الطرق والخوارزميات المتّبعة في هذا المجال، وعرض لقواعد البيانات العالمية والطرق المتبعة في الاختبار، ثم ننتقل إلى تصميم نظام للتعرف على النشاط البشري وتنفيذه في بيئة MATLAB.
In this work, our goal is recognizing human action from video data. First we propose an overview about Human Action Recognition, includes the famous methods and previous algorithms, then we propose an algorithm and its implementation using MATLAB.
References used
Wannous Bashar, Jaafar Assef, and Albitar Chadi. "Human Action Recognition using Contour History Images and Neural Networks Classifier." International Research Journal of Engineering and Technology 4.8 (2017): 7
Turaga, Pavan, et al. "Machine recognition of human activities: A survey." IEEE Transactions on Circuits and Systems for Video Technology 18.11 (2008): 1473-1488
Aggarwal, Jake K., and Michael S. Ryoo. "Human activity analysis: A review." ACM Computing Surveys (CSUR) 43.3 (2011): 16
rate research
Read More
Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limite
d. In this work, we take this research direction to the opposite and study cross-domain data augmentation for the NER task. We investigate the possibility of leveraging data from high-resource domains by projecting it into the low-resource domains. Specifically, we propose a novel neural architecture to transform the data representation from a high-resource to a low-resource domain by learning the patterns (e.g. style, noise, abbreviations, etc.) in the text that differentiate them and a shared feature space where both domains are aligned. We experiment with diverse datasets and show that transforming the data to the low-resource domain representation achieves significant improvements over only using data from high-resource domains.
We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video. Existing methods of multi-modal grammar induction focus on grammar induction from text-image pairs, with promising
results showing that the information from static images is useful in induction. However, videos provide even richer information, including not only static objects but also actions and state changes useful for inducing verb phrases. In this paper, we explore rich features (e.g. action, object, scene, audio, face, OCR and speech) from videos, taking the recent Compound PCFG model as the baseline. We further propose a Multi-Modal Compound PCFG model (MMC-PCFG) to effectively aggregate these rich features from different modalities. Our proposed MMC-PCFG is trained end-to-end and outperforms each individual modality and previous state-of-the-art systems on three benchmarks, i.e. DiDeMo, YouCook2 and MSRVTT, confirming the effectiveness of leveraging video information for unsupervised grammar induction.
الغاية من هذا البحث بناء نظام لتصنيف نطق الأرقام الانكليزية وذلك بالاعتماد على نماذج ماركوف المخفية في التصنيف وذلك بالاعتماد على طيف الإشارة في استخراج سمات الإشارات
Temporal language grounding in videos aims to localize the temporal span relevant to the given query sentence. Previous methods treat it either as a boundary regression task or a span extraction task. This paper will formulate temporal language groun
ding into video reading comprehension and propose a Relation-aware Network (RaNet) to address it. This framework aims to select a video moment choice from the predefined answer set with the aid of coarse-and-fine choice-query interaction and choice-choice relation construction. A choice-query interactor is proposed to match the visual and textual information simultaneously in sentence-moment and token-moment levels, leading to a coarse-and-fine cross-modal interaction. Moreover, a novel multi-choice relation constructor is introduced by leveraging graph convolution to capture the dependencies among video moment choices for the best choice selection. Extensive experiments on ActivityNet-Captions, TACoS, and Charades-STA demonstrate the effectiveness of our solution. Codes will be available at https://github.com/Huntersxsx/RaNet.
Meta-learning has recently been proposed to learn models and algorithms that can generalize from a handful of examples. However, applications to structured prediction and textual tasks pose challenges for meta-learning algorithms. In this paper, we a
pply two meta-learning algorithms, Prototypical Networks and Reptile, to few-shot Named Entity Recognition (NER), including a method for incorporating language model pre-training and Conditional Random Fields (CRF). We propose a task generation scheme for converting classical NER datasets into the few-shot setting, for both training and evaluation. Using three public datasets, we show these meta-learning algorithms outperform a reasonable fine-tuned BERT baseline. In addition, we propose a novel combination of Prototypical Networks and Reptile.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
