Multimodal research in vision and language: A review of current and emerging trends
By Shagun Uppal, Sarthak Bhagat, Devamanyu Hazarika, Navonil Majumder, Soujanya Poria, Roger Zimmermann, Amir Zadeh. 2022
109 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Multimodal Learning
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In Artificial Intelligence
In Computer Vision and Pattern Recognition
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Citations in the corpus
- SimVLM: Simple Visual Language Model Pretraining with Weak Supervision (2021, background): “Recent years have seen a rapid progress made in vision-language pretraining (Uppal et al., 2020; Han et al., 2021; Khan et al., 2021).”
- WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research (2023, background): “While AL research is relatively young, vision-language (VL) multimodal learning [21], the counterpart of AL multimodal learning, has been studied for decades and has contributed to the success of many vision-language applications, such as cross-modal search [22], image and video captioning [23],…”
- A Survey on Mixture of Experts in Large Language Models (2024, unknown): “These models often simultaneously encompass two primary data modalities: images and text [180], [181], [182].”