# Beyond VQA: Generating Multi-word Answer and Rationale to Visual Questions

Source: [Beyond VQA: Generating Multi-word Answer and Rationale to Visual Questions](<https://www.sota2.com/research/paper/68fa6a271e209131cea26629>)

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

Data updated: 2025-10-25T23:17:37.192Z

Authors: Radhika Dua, Sai Srinivas Kancheti, Vineeth N Balasubramanian

Published: 2020-10-24T09:44:50.000Z

## Abstract

Visual Question Answering is a multi-modal task that aims to measure high-level visual understanding. Contemporary VQA models are restrictive in the sense that answers are obtained via classification over a limited vocabulary (in the case of open-ended VQA), or via classification over a set of multiple-choice-type answers. In this work, we present a completely generative formulation where a multi-word answer is generated for a visual query. To take this a step forward, we introduce a new task: ViQAR (Visual Question Answering and Reasoning), wherein a model must generate the complete answer and a rationale that seeks to justify the generated answer. We propose an end-to-end architecture to solve this task and describe how to evaluate it. We show that our model generates strong answers and rationales through qualitative and quantitative evaluation, as well as through a human Turing Test.

## 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/2010.12852>)

[PDF](<https://arxiv.org/pdf/2010.12852.pdf>)