# Inverse Visual Question Answering with Multi-Level Attentions

Source: [Inverse Visual Question Answering with Multi-Level Attentions](<https://www.sota2.com/research/paper/68fa68d51e209131cea1e7ba>)

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

Data updated: 2025-10-25T22:46:52.640Z

Authors: Yaser Alwattar, Yuhong Guo

Published: 2019-09-17T04:41:12.000Z

## Abstract

In this paper, we propose a novel deep multi-level attention model to address inverse visual question answering. The proposed model generates regional visual and semantic features at the object level and then enhances them with the answer cue by using attention mechanisms. Two levels of multiple attentions are employed in the model, including the dual attention at the partial question encoding step and the dynamic attention at the next question word generation step. We evaluate the proposed model on the VQA V1 dataset. It demonstrates state-of-the-art performance in terms of multiple commonly used metrics.

## 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/1909.07583>)

[PDF](<https://arxiv.org/pdf/1909.07583.pdf>)