Designing efficient and reliable VQA systems remains a challenging problem,
more so in the case of disaster management and response systems. In this work,
we revisit fundamental combination methods like concatenation, addition and
element-wise multiplication with modern image and text feature abstraction
models. We design a simple and efficient system which outperforms pre-existing
methods on the FloodNet dataset and achieves state-of-the-art performance. This
simplified system requires significantly less training and inference time than
modern VQA architectures. We also study the performance of various backbones
and report their consolidated results. Code is available at
this https URL.

本文设计了一个基于现代图像和文本特征抽象模型的简单而高效的 VQA 系统，使用基本的组合方法，如拼接，加法和逐元素乘法，在灾害管理和响应系统中具有更高的性能表现。该系统在 FloodNet 数据集上超过现有方法并达到了最先进的性能，需要的训练和推理时间也更少。

FloodNet VQA 的高效现代基线

An Efficient Modern Baseline for FloodNet VQA

Visual scene understanding is the core task in making any crucial decision in
any computer vision system. Although popular computer vision datasets like
Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tasks (e.g.
image classification, segmentation, object detection), these datasets are
hardly suitable for post disaster damage assessments. On the other hand,
existing natural disaster datasets include mainly satellite imagery which have
low spatial resolution and a high revisit period. Therefore, they do not have a
scope to provide quick and efficient damage assessment tasks. Unmanned Aerial
Vehicle(UAV) can effortlessly access difficult places during any disaster and
collect high resolution imagery that is required for aforementioned tasks of
computer vision. To address these issues we present a high resolution UAV
imagery, FloodNet, captured after the hurricane Harvey. This dataset
demonstrates the post flooded damages of the affected areas. The images are
labeled pixel-wise for semantic segmentation task and questions are produced
for the task of visual question answering. FloodNet poses several challenges
including detection of flooded roads and buildings and distinguishing between
natural water and flooded water. With the advancement of deep learning
algorithms, we can analyze the impact of any disaster which can make a precise
understanding of the affected areas. In this paper, we compare and contrast the
performances of baseline methods for image classification, semantic
segmentation, and visual question answering on our dataset.

本文提出了一种高分辨率无人机图像数据集 FloodNet，用于帮助解决自然灾害后的损害评估问题，数据集提出了 “洪水下的损害” 这个问题，并对其进行了像素级的语义分割标注，提出一些深度学习算法用于解决这些挑战，并比较了该数据集上多个图像任务算法的性能表现。