The multi-modal fusion in visual question answering: a review of attention mechanisms
Abstract Visual Question Answering (VQA) is a significant cross-disciplinary issue in the
fields of computer vision and natural language processing that requires a computer to output …
fields of computer vision and natural language processing that requires a computer to output …
Foundations & trends in multimodal machine learning: Principles, challenges, and open questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
Vision-language pre-training: Basics, recent advances, and future trends
This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …
intelligence that have been developed in the last few years. We group these approaches …
A metaverse: Taxonomy, components, applications, and open challenges
SM Park, YG Kim - IEEE access, 2022 - ieeexplore.ieee.org
Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …
based on the social value of Generation Z that online and offline selves are not different …
Focal self-attention for local-global interactions in vision transformers
Recently, Vision Transformer and its variants have shown great promise on various
computer vision tasks. The ability of capturing short-and long-range visual dependencies …
computer vision tasks. The ability of capturing short-and long-range visual dependencies …
Scaling up visual and vision-language representation learning with noisy text supervision
Pre-trained representations are becoming crucial for many NLP and perception tasks. While
representation learning in NLP has transitioned to training on raw text without human …
representation learning in NLP has transitioned to training on raw text without human …
Vitae: Vision transformer advanced by exploring intrinsic inductive bias
Transformers have shown great potential in various computer vision tasks owing to their
strong capability in modeling long-range dependency using the self-attention mechanism …
strong capability in modeling long-range dependency using the self-attention mechanism …
[PDF][PDF] Large-scale domain-specific pretraining for biomedical vision-language processing
Contrastive pretraining on parallel image-text data has attained great success in vision-
language processing (VLP), as exemplified by CLIP and related methods. However, prior …
language processing (VLP), as exemplified by CLIP and related methods. However, prior …
Tedigan: Text-guided diverse face image generation and manipulation
In this work, we propose TediGAN, a novel framework for multi-modal image generation and
manipulation with textual descriptions. The proposed method consists of three components …
manipulation with textual descriptions. The proposed method consists of three components …
Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …