Visual Question Answering: Datasets, Algorithms, And Future Challenges | Awesome LLM Papers Add your paper to Awesome LLM Papers

Awesome LLM Papers

A continuously updated collection of research papers on LLMs.

Stay Updated

Contact Sean Moran about this survey or website. Made with Jekyll and Hyde.

Visual Question Answering: Datasets, Algorithms, And Future Challenges

Kushal Kafle, Christopher Kanan . Computer Vision and Image Understanding 2017 – 160 citations

[Paper]   Search on Google Scholar   Search on Semantic Scholar
3d Representation Compositional Generalization Content Enrichment Datasets Evaluation Image Text Integration Interactive Environments Interdisciplinary Approaches Multimodal Semantic Representation Neural Machine Translation Productivity Enhancement Question Answering Variational Autoencoders Visual Contextualization Visual Question Answering

Visual Question Answering (VQA) is a recent problem in computer vision and natural language processing that has garnered a large amount of interest from the deep learning, computer vision, and natural language processing communities. In VQA, an algorithm needs to answer text-based questions about images. Since the release of the first VQA dataset in 2014, additional datasets have been released and many algorithms have been proposed. In this review, we critically examine the current state of VQA in terms of problem formulation, existing datasets, evaluation metrics, and algorithms. In particular, we discuss the limitations of current datasets with regard to their ability to properly train and assess VQA algorithms. We then exhaustively review existing algorithms for VQA. Finally, we discuss possible future directions for VQA and image understanding research.

Similar Work