Advances in deep learning approaches for image tagging

Fu, Jianlong; Rui, Yong

doi:10.1017/atsip.2017.12

Cited by 43 publications

(24 citation statements)

References 75 publications

(125 reference statements)

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“…Inevitably, some adjustments are required, such as different image categories (for social media content) or devising other image features from satellite data. Current advances in automated satellite processing (Fu & Rui, 2017) and social media content analysis (Gosal et al, 2019), together with increasing availability of satellite data with enhanced coverage (e.g., Guanter et al, 2015) and developments in powerful geospatial analysis platforms (e.g., Gorelick et al, 2017), will boost the assessment of nature's cultural contributions to people and the monitoring of conservation targets in wider areas.…”

Section: Discussionmentioning

confidence: 99%

Digital conservation in biosphere reserves: Earth observations, social media, and nature's cultural contributions to people

Vaz

Moreno-Llorca

Gonçalves³

et al. 2020

CONSERVATION LETTERS

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In the "digital conservation" age, big data from Earth observations and from social media have been increasingly used to tackle conservation challenges. Here, we combined information from those two digital sources in a multimodel inference framework to identify, map, and predict the potential for nature's cultural contributions to people in two contrasting UNESCO biosphere reserves: Doñana and Sierra Nevada (Spain). The content analysis of Flickr pictures revealed different cultural contributions, according to the natural and cultural values of the two reserves. Those contributions relied upon landscape variables computed from Earth observation data: the variety of colors and vegetation functioning that characterize Doñana landscapes, and the leisure facilities, accessibility features, and heterogeneous landscapes that shape Sierra Nevada. Our findings suggest that social media and Earth observations can aid in the cost-efficient monitoring of nature's contributions to people, which underlie many Sustainable Development Goals and conservation targets in protected areas worldwide. K E Y W O R D Sbig data, crowdsourced photos, cultural values, Doñana, ecosystem services, multimodel inference, protected areas, participatory sensing, remote sensing, Sierra NevadaThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Discussionmentioning

confidence: 99%

Digital conservation in biosphere reserves: Earth observations, social media, and nature's cultural contributions to people

Vaz

Moreno-Llorca

Gonçalves³

et al. 2020

CONSERVATION LETTERS

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“…What is not clear from Microsoft's documentation is how big their training set is, or how the training images were first annotated. Microsoft Research provides links to papers and videos its researchers have produced; Fu and Rui [44] survey the various approaches to automated image tagging. At the time of their writing, they point to a combination of noisy datasets used for training (such as Flickr photos tagged by humans) and contextual clues (using word-embeddings; see [45] for an introduction to these) to learn the probabilities that tags 'go together' in meaningful ways (or to put it another way, some tags preclude the likelihood of others).…”

Section: Methodsmentioning

confidence: 99%

Towards a Digital Sensorial Archaeology as an Experiment in Distant Viewing of the Trade in Human Remains on Instagram

2020

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It is possible to purchase human remains via Instagram. We present an experiment using computer vision and automated annotation of over ten thousand photographs from Instagram, connected with the buying and selling of human remains, in order to develop a distant view of the sensory affect of these photos: What macroscopic patterns exist, and how do these relate to the self-presentation of these individual vendors? Using Microsoft’s Azure cloud computing and machine learning services, we annotate and then visualize the co-occurrence of tags as a series of networks, giving us that macroscopic view. Vendors are clearly trying to mimic ‘museum’-like experiences, with differing degrees of effectiveness. This approach may therefore be useful for even larger-scale investigations of this trade beyond this single social media platform.

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“…And the final model size is 2.60MB which is very lightweight to execute in any mobile platform. Where some well-known models like VGG, Reset has size more than 200MB [25]. After 100 epochs, 81.35% and 51.59% training accuracy have been achieved for gender and age respectively.…”

Section: A Performance Analysismentioning

confidence: 99%

A Lightweight Deep Convolutional Neural Network Model for Real-Time Age and Gender Prediction

Opu

Koly

Das

et al. 2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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Recognition of age and gender has become a significant part of the biometric system, protection, and treatment. It is widely used for people to access age-related content. It is used by social media in the distribution of layered advertising and promotions to expand its scope. Application of face detection has grown to a great extent that we should upgrade it using various methods to achieve more accurate results. In this paper, we have developed a lightweight deep Convolution neural network model for real-time age and gender prediction. For making the training dataset more diverse, Wiki, UTKFace, and Adience datasets have been merged into one containing 18728 images. Using this vast mixed dataset, we have achieved accuracy of 48.59% and 80.76% for age and gender respectively. Further, the model is tested in real-time. Different experimental investigations on the prepared dataset show that with most recent approaches, our model provides competitive prediction accuracy.

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Advances in deep learning approaches for image tagging

Abstract: Advances in deep learning approaches for image tagging jianlong fu and yong rui

Cited by 43 publications

References 75 publications

Digital conservation in biosphere reserves: Earth observations, social media, and nature's cultural contributions to people

Digital conservation in biosphere reserves: Earth observations, social media, and nature's cultural contributions to people

Towards a Digital Sensorial Archaeology as an Experiment in Distant Viewing of the Trade in Human Remains on Instagram

A Lightweight Deep Convolutional Neural Network Model for Real-Time Age and Gender Prediction

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