Low Latency Deep Learning Based Parking Occupancy Detection By Exploiting Structural Similarity

Ng, Chin-Kit; Cheong, Soon-Nyean; Foo, Yee-Loo

doi:10.1007/978-981-15-0058-9_24

Cited by 11 publications

(2 citation statements)

References 6 publications

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“…In all the BERT2BERT models, we make use of the same BERT model in the encoder and the decoder parts. Lastly, it is known that using more data when training deep learning models usually tends to result in better performances (Ng et al 2014). We further investigate this notion with the same set of models on the Combined-TR dataset which we have created by merging the TR-News and MLSum (TR) datasets.…”

Section: Experiments 1-summary Generationmentioning

confidence: 99%

Turkish abstractive text summarization using pretrained sequence-to-sequence models

Baykara

Güngör

2022

Nat. Lang. Eng.

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The tremendous amount of increase in the number of documents available on the Web has turned finding the relevant piece of information into a challenging, tedious, and time-consuming activity. Accordingly, automatic text summarization has become an important field of study by gaining significant attention from the researchers. Lately, with the advances in deep learning, neural abstractive text summarization with sequence-to-sequence (Seq2Seq) models has gained popularity. There have been many improvements in these models such as the use of pretrained language models (e.g., GPT, BERT, and XLM) and pretrained Seq2Seq models (e.g., BART and T5). These improvements have addressed certain shortcomings in neural summarization and have improved upon challenges such as saliency, fluency, and semantics which enable generating higher quality summaries. Unfortunately, these research attempts were mostly limited to the English language. Monolingual BERT models and multilingual pretrained Seq2Seq models have been released recently providing the opportunity to utilize such state-of-the-art models in low-resource languages such as Turkish. In this study, we make use of pretrained Seq2Seq models and obtain state-of-the-art results on the two large-scale Turkish datasets, TR-News and MLSum, for the text summarization task. Then, we utilize the title information in the datasets and establish hard baselines for the title generation task on both datasets. We show that the input to the models has a substantial amount of importance for the success of such tasks. Additionally, we provide extensive analysis of the models including cross-dataset evaluations, various text generation options, and the effect of preprocessing in ROUGE evaluations for Turkish. It is shown that the monolingual BERT models outperform the multilingual BERT models on all tasks across all the datasets. Lastly, qualitative evaluations of the generated summaries and titles of the models are provided.

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Section: Experiments 1-summary Generationmentioning

confidence: 99%

Turkish abstractive text summarization using pretrained sequence-to-sequence models

Baykara

Güngör

2022

Nat. Lang. Eng.

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“…While such approaches can provide highly accurate information to a PGI system, they require additional costs in terms of sensors cost, installation, and maintenance. Recently, parking slot occupancy detection based on computer vision emerged as a promising source of information for PGI systems Cai et al (2019); Acharya et al (2018); Ng et al (2020); Amato et al (2017). These solutions extract information about parking occupancy from images obtained by a camera that is recording parking area from a certain viewpoint such that parking slots are at least partially visible.…”

Section: Introductionmentioning

confidence: 99%

Automatic vision-based parking slot detection and occupancy classification

Grbić¹,

Brando²

2023

Expert Systems with Applications

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Big Data and Internet of Things for Smart Data Analytics Using Machine Learning Techniques

Jane¹,

Ganesh²

2020

Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019)

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Low Latency Deep Learning Based Parking Occupancy Detection By Exploiting Structural Similarity

Cited by 11 publications

References 6 publications

Turkish abstractive text summarization using pretrained sequence-to-sequence models

Turkish abstractive text summarization using pretrained sequence-to-sequence models

Automatic vision-based parking slot detection and occupancy classification

Big Data and Internet of Things for Smart Data Analytics Using Machine Learning Techniques

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