Image Classification in Arabic: Exploring Direct English to Arabic Translations

Alsudais, Abdulkareem

doi:10.1109/access.2019.2926924

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…Since then, several authors have attempted to extend it by improving its coverage or quality (Alkhalifa and Rodríguez, 2009;Abouenour et al, 2013;Bond and Foster, 2013;Regragui et al, 2016;Batita et al, 2019). The possibility of using AWN to extend ImageNet has been experimented with in one paper (Alsudais, 2019). In the paper, the author tested using AWN to find Arabic synsets for a small sample of 100 images from ImageNet and indicated that Arabic synsets were found for only six synsets.…”

Section: ‫كلب‬mentioning

confidence: 99%

“…In other papers, attempts to connect ImageNet to external resources were made by linking the synsets in ImageNet to items in Wikidata (Nielsen, 2018) and by extending a 1 http://www.image-net.org/download.php sample of images in ImageNet to German using human subjects, which resulted in a dataset of 1,305,602 images (Roller and Schulte, 2013). In the only other closely related paper, the author investigated the possibility of generating Arabic labels for images in ImageNet using an online translator (Alsudais, 2019). In the paper, the author targeted a sample of 1,895 images from ImageNet and used an online translator to generate Arabic labels for the synsets.…”

Section: ‫كلب‬mentioning

confidence: 99%

“…The successes of these systems have primarily been a result of variational autoencoders (VAEs) (Kingma and Welling, 2013). Conversely, VQG in the medical domain is still a challenging and under-explored task (Hasan et al, 2018;, 2019.…”

Section: Related Workmentioning

confidence: 99%

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Proceedings of the First Workshop on Advances in Language and Vision Research

2020

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Workshop schedule details: https://alvr-workshop.github.ioThe workshop also holds the first Video-guided Machine Translation (VMT) challenge and the REVERIE challenge. The VMT challenge aims to benchmark progress towards models that translate source language sentence into the target language with video information as the additional spatiotemporal context. The challenge is based on the recently released large-scale multilingual video description dataset, VA-TEX. The VATEX dataset contains over 41,250 videos and 825,000 high-quality captions in both English and Chinese, half of which are English-Chinese translation pairs. The REVERIE challenge requires an intelligent agent to correctly localize a remote target object (cannot be observed at the starting location) specified by a concise high-level natural language instruction, such as "bring me the blue cushion from the sofa in the living room". Since the target object is in a different room from the starting one, the agent needs first to navigate to the goal location. When the agent determines to stop, it should select one object from a list of candidates provided by the simulator. The agent can attempt to localize the target at any step, which is totally up to algorithm design. But the agent is only allowed to output once in each episode, which means the agent only can guess the answer once in a single run.

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Section: ‫كلب‬mentioning

confidence: 99%

Section: ‫كلب‬mentioning

confidence: 99%

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Proceedings of the First Workshop on Advances in Language and Vision Research

2020

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“…In other papers, attempts to connect ImageNet to external resources were made by linking the synsets in ImageNet to items in Wikidata (Nielsen, 2018) and by extending a sample of images in ImageNet to German using human subjects, which resulted in a dataset of 1,305,602 images (Roller and Schulte, 2013). In the only other closely related paper, the author investigated the possibility of generating Arabic labels for images in ImageNet using an online translator (Alsudais, 2019). In the paper, the author targeted a sample of 1,895 images from ImageNet and used an online translator to generate Arabic labels for the synsets.…”

Section: Abdulkareem Alsudaismentioning

confidence: 99%

Extending ImageNet to Arabic using Arabic WordNet

Alsudais¹

2020

Proceedings of the First Workshop on Advances in Language and Vision Research

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ImageNet has millions of images that are labeled with English WordNet synsets. This paper investigates the extension of ImageNet to Arabic using Arabic WordNet. The objective is to discover if Arabic synsets can be found for synsets used in ImageNet. The primary finding is the identification of Arabic synsets for 1,219 of the 21,841 synsets used in ImageNet, which represents 1.1 million images. By leveraging the parent-child structure of synsets in ImageNet, this dataset is extended to 10,462 synsets (and 7.1 million images) that have an Arabic label, which is either a match or a direct hypernym, and to 17,438 synsets (and 11 million images) when a hypernym of a hypernym is included. When all hypernyms for a node are considered, an Arabic synset is found for all but four synsets. This represents the major contribution of this work: a dataset of images that have Arabic labels for 99.9% of the images in ImageNet.

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Extending ImageNet to Arabic using Arabic WordNet

Alsudais

2022

Multimed Tools Appl

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Image Classification in Arabic: Exploring Direct English to Arabic Translations

Cited by 6 publications

References 37 publications

Proceedings of the First Workshop on Advances in Language and Vision Research

Proceedings of the First Workshop on Advances in Language and Vision Research

Extending ImageNet to Arabic using Arabic WordNet

Extending ImageNet to Arabic using Arabic WordNet

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