A Review on Explainability in Multimodal Deep Neural Nets

by   Gargi Joshi, et al.

Artificial Intelligence techniques powered by deep neural nets have achieved much success in several application domains, most significantly and notably in the Computer Vision applications and Natural Language Processing tasks. Surpassing human-level performance propelled the research in the applications where different modalities amongst language, vision, sensory, text play an important role in accurate predictions and identification. Several multimodal fusion methods employing deep learning models are proposed in the literature. Despite their outstanding performance, the complex, opaque and black-box nature of the deep neural nets limits their social acceptance and usability. This has given rise to the quest for model interpretability and explainability, more so in the complex tasks involving multimodal AI methods. This paper extensively reviews the present literature to present a comprehensive survey and commentary on the explainability in multimodal deep neural nets, especially for the vision and language tasks. Several topics on multimodal AI and its applications for generic domains have been covered in this paper, including the significance, datasets, fundamental building blocks of the methods and techniques, challenges, applications, and future trends in this domain


page 14

page 24


Multimodal Intelligence: Representation Learning, Information Fusion, and Applications

Deep learning has revolutionized speech recognition, image recognition, ...

Explainability of vision-based autonomous driving systems: Review and challenges

This survey reviews explainability methods for vision-based self-driving...

Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?

The recent series of innovations in deep learning (DL) have shown enormo...

DecisiveNets: Training Deep Associative Memories to Solve Complex Machine Learning Problems

Learning deep representations to solve complex machine learning tasks ha...

New Ideas and Trends in Deep Multimodal Content Understanding: A Review

The focus of this survey is on the analysis of two modalities of multimo...

Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey

Despite being responsible for state-of-the-art results in several comput...

Please sign up or login with your details

Forgot password? Click here to reset