# Open-Ended Long-Form Video Question Answering via Hierarchical Convolutional Self-Attention Networks

Source: [Open-Ended Long-Form Video Question Answering via Hierarchical Convolutional Self-Attention Networks](<https://www.sota2.com/research/paper/68fa65f41e209131cea0d5c1>)

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

Data updated: 2025-10-25T21:39:29.972Z

Authors: Zhu Zhang, Zhou Zhao, Zhijie Lin, Jingkuan Song, Xiaofei He

Published: 2019-06-28T12:23:37.000Z

## Abstract

Open-ended video question answering aims to automatically generate the natural-language answer from referenced video contents according to the given question. Currently, most existing approaches focus on short-form video question answering with multi-modal recurrent encoder-decoder networks. Although these works have achieved promising performance, they may still be ineffectively applied to long-form video question answering due to the lack of long-range dependency modeling and the suffering from the heavy computational cost. To tackle these problems, we propose a fast Hierarchical Convolutional Self-Attention encoder-decoder network(HCSA). Concretely, we first develop a hierarchical convolutional self-attention encoder to efficiently model long-form video contents, which builds the hierarchical structure for video sequences and captures question-aware long-range dependencies from video context. We then devise a multi-scale attentive decoder to incorporate multi-layer video representations for answer generation, which avoids the information missing of the top encoder layer. The extensive experiments show the effectiveness and efficiency of our method.

## 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.

- [Video Question Answering on OL-VideoQA (test)](<https://www.sota2.com/research/sota/video-question-answering-on-ol-videoqa-test.md>): Video Question Answering; OL-VideoQA (test); BLEU-1: 29.85.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.12158>)

[PDF](<https://arxiv.org/pdf/1906.12158.pdf>)