Answer grounding is the task of locating relevant visual evidence for the
Visual Question Answering task. While a wide variety of attention methods have
been introduced for this task, they suffer from the following three problems:
designs that do not allow the usage of pre-trained networks and do not benefit
from large data pre-training, custom designs that are not based on
well-grounded previous designs, therefore limiting the learning power of the
network, or complicated designs that make it challenging to re-implement or
improve them. In this paper, we propose a novel architectural block, which we
term Sentence Attention Block, to solve these problems. The proposed block
re-calibrates channel-wise image feature-maps by explicitly modeling
inter-dependencies between the image feature-maps and sentence embedding. We
visually demonstrate how this block filters out irrelevant feature-maps
channels based on sentence embedding. We start our design with a well-known
attention method, and by making minor modifications, we improve the results to
achieve state-of-the-art accuracy. The flexibility of our method makes it easy
to use different pre-trained backbone networks, and its simplicity makes it
easy to understand and be re-implemented. We demonstrate the effectiveness of
our method on the TextVQA-X, VQS, VQA-X, and VizWiz-VQA-Grounding datasets. We
perform multiple ablation studies to show the effectiveness of our design
choices.

提出了一种名为 “Sentence Attention Block” 的新型建筑模块，通过明确建模图像特征图与句子嵌入之间的相互依赖关系，重新校准了通道级图像特征图，以过滤基于句子嵌入的不相关特征图通道，进而实现了目前最先进准确度的提高。