# Multi-Image Visual Question Answering

Source: [Multi-Image Visual Question Answering](<https://www.sota2.com/research/paper/68fa6bc61e209131cea303b3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:57:05.246Z

Authors: Harsh Raj, Janhavi Dadhania, Akhilesh Bhardwaj, Prabuchandran KJ

Published: 2021-12-27T14:28:04.000Z

## Abstract

While a lot of work has been done on developing models to tackle the problem of Visual Question Answering, the ability of these models to relate the question to the image features still remain less explored. We present an empirical study of different feature extraction methods with different loss functions. We propose New dataset for the task of Visual Question Answering with multiple image inputs having only one ground truth, and benchmark our results on them. Our final model utilising Resnet + RCNN image features and Bert embeddings, inspired from stacked attention network gives 39% word accuracy and 99% image accuracy on CLEVER+TinyImagenet dataset.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2112.13706>)

[PDF](<https://arxiv.org/pdf/2112.13706.pdf>)