Joint Image-text Representation Learning for Fashion Retrieval

Yan, Cairong; Yu, Li; Wan, Yongquan; Zhang, Zhaohui

doi:10.1145/3383972.3384048

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…By default both dropconnect[29] and dropout are used with a conservative rate of 0.2 and the following types of data augmentation are implemented - random rotations (−10 to +10 degrees), random zooms/crops (0.9 - 1.25x), random flipping, and random elastic deformations. The amount of elastic deformation is kept small as large deformations may not be suitable for all applications.…”

Section: System Overviewmentioning

confidence: 99%

No-code machine learning in radiology: implementation and validation of a platform that allows clinicians to train their own models

Elton,

Dasegowda,

Sato

et al. 2024

Preprint

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Machine learning models can assist clinicians and researchers in many tasks within radiology such as diagnosis, triage, segmentation/measurement, and quality assurance. To better leverage machine learning we have developed a platform that allows users to label data and train models without requiring any programming knowledge. The technology stack consists of a TypeScript web application running on .NET for user interaction, Python, PyTorch, and MONAI for machine learning, DICOM WADO-RS to retrieve data from clinical systems, and Docker for model management. As a first trial of the system, researchers used it to train a model for clavicle fracture detection as part of an IRB-approved retrospective study. The researchers labeled 4,135 clavicle radiographs from 2,039 patients across 13 sites. The platform automatically split the data into training, validation, and test sets and trained a model until the validation loss plateaued. The system then returned a receiver operating characteristic curve, AUC, F1, and other metrics. The resulting model identifies clavicle fractures with 90% sensitivity, 87% specificity, and 88% accuracy with an AUC of 0.95. This model performance is equivalent to or better than similar models reported in the literature. More recently, our system was used to train a model to identify if ultrasound frames that contain personally identifiable information (PII). After validation, the model was used to help de-identify a large dataset that was to be used for research. This first-of-its-kind system streamlines model development and deployment and opens up an exciting new pathway for the use of AI within healthcare.

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Section: System Overviewmentioning

confidence: 99%

No-code machine learning in radiology: implementation and validation of a platform that allows clinicians to train their own models

Elton,

Dasegowda,

Sato

et al. 2024

Preprint

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show abstract

“…In the signal processing domain, recognition [403] and analysis [404] each have 1; in the gender domain, classification [405] and recognition [406] each also have 1 article. The industry, fashion, e-commerce, energy, mapping, academia, product, food, cooking, plant, language, chemistry and mobile application domains have 1 relevant article in the following applications accordingly: prediction [407], retrieval [408], classification [409], classification [410], prediction [411], recognition [412], recognition [413], inspection [414], retrieval [415], segmentation [416], translation [417], analysis [418], and detection [419].…”

Section: Inclusion Criteriamentioning

confidence: 99%

“…[138], [139], [160], [176], [178], [179], [185]- [187], [198], [202], [216], [217], [220], [221], [230], [236], [257], [266], [267], [272], [275], [301], [317], [345], [348], [352], [356], [378], [407], [408], [418], [419], 10 [49], [112], [113], [128], [193], [203], [227], [249], [293], [383] , and 14 [73], [94],…”

Section: [68]-[72] [77] [88]-[90] [92] [93]mentioning

confidence: 99%

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

2023

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Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role by tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions.

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“…Another significant area of study focuses on gathering data from consumers using sentiment analysis techniques, such as via reviews or social media [42,44,[169][170][171][172][173][174][175][176][177][178][179][180]. Finally, studies on picture tagging using textual content analysis have been conducted [55,61,73,89,94,95,98,104,107,113,116,118,[181][182][183][184].…”

Section: Ai In B2c Retail and Application Areas Of The Techniquesmentioning

confidence: 99%

“…Amazon5-core [163] AmazonDresses [94] AmazonFashion [122] Amazonfashiondataset [73] ane-commerceplatform [50] ASOS [124] CCP [97] Clothing1M [70] Collar-6 [49] Collected [76,91-93,96,98,100,110,112,114,136,137,145,149,151,152,201,…”

Section: Databases Citationsmentioning

confidence: 99%

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

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Many industries, including healthcare, banking, the auto industry, education, and retail, have already undergone significant changes because of artificial intelligence (AI). Business-to-Customer (B2C) e-commerce has considerably increased the use of AI in recent years. The purpose of this research is to examine the significance and impact of AI in the realm of fashion e-commerce. To that end, a systematic review of the literature is carried out, in which data from the Web Of Science and Scopus databases were used to analyze 219 publications on the subject. The articles were first categorized using AI techniques. In the realm of fashion e-commerce, they were divided into two categories. These categorizations allowed for the identification of research gaps in the use of AI. These gaps offer potential and possibilities for further research.

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Joint Image-text Representation Learning for Fashion Retrieval

Cited by 5 publications

References 12 publications

No-code machine learning in radiology: implementation and validation of a platform that allows clinicians to train their own models

No-code machine learning in radiology: implementation and validation of a platform that allows clinicians to train their own models

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

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