Heterogeneous Transfer with Deep Latent Correlation for Sentiment Analysis

Cai, Guoyong; Lv, Guangrui

doi:10.1109/iscid.2017.172

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…In total, 26 distinct combinations were identified using data of six different modalities. [188], [262], [265], [273], [274], [277], [294], [300], [376], [379], [393], Video & Audio & Sensor [252], [296], [409], Video & Audio [153], [171], [229], [253], [255], [266], [275], [281], [287], [292], [295], [298], [315], Video & Text [199], Video & Sensor [271], Video & Signal [250], [283], Image & Audio & Text [111], [175], [204], [213], [216], [263], [264], [288], [340], [396], [406], Image & Audio & Sensor & Signal [366], Image & Audio & Sensor [249], [334], [335], Image & Audio …”

Section: B Taskmentioning

confidence: 99%

“…[95], [116], [168], [229], [244], [273], [274], [289], [300], [336], [355], [362] articles, respectively. In total, 7 and 5 articles related to similarity [48], [51], [125], [170], [171], [268], [338] and structured [64], [199], [211], [212], [262]…”

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

confidence: 99%

“…The similarity models work on the distances of two modalities [171], [338]. Structured models are typically used in crossed hashing [64], such as canonical correlation analysis (CCA) [199], [212]. In comparison, joint representation is best suited for all modalities as opposed to a coordinated representation suitable for application when only one modality is present at test time.…”

Section: B Inspection Of Rq2mentioning

confidence: 99%

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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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