The widely used Fact-based Visual Question Answering (FVQA) dataset contains
visually-grounded questions that require information retrieval using common
sense knowledge graphs to answer. It has been observed that the original
dataset is highly imbalanced and concentrated on a small portion of its
associated knowledge graph. We introduce FVQA 2.0 which contains adversarial
variants of test questions to address this imbalance. We show that systems
trained with the original FVQA train sets can be vulnerable to adversarial
samples and we demonstrate an augmentation scheme to reduce this vulnerability
without human annotations.

FVQA 2.0 数据集添加了敌对测试问题，以解决此数据集高度不平衡及集中在其相关知识图谱的部分问题。实验结果表明，原始数据集训练的系统容易受到敌对样本的影响，但通过无需人工标记的增强方法可以减少此类漏洞。

FVQA 2.0：将对抗样本引入基于事实的视觉问答

FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering

Zero-shot learning relies on semantic class representations such as
hand-engineered attributes or learned embeddings to predict classes without any
labeled examples. We propose to learn class representations by embedding nodes
from common sense knowledge graphs in a vector space. Common sense knowledge
graphs are an untapped source of explicit high-level knowledge that requires
little human effort to apply to a range of tasks. To capture the knowledge in
the graph, we introduce ZSL-KG, a general-purpose framework with a novel
transformer graph convolutional network (TrGCN) for generating class
representations. Our proposed TrGCN architecture computes non-linear
combinations of node neighbourhoods. Our results show that ZSL-KG improves over
existing WordNet-based methods on five out of six zero-shot benchmark datasets
in language and vision.

使用 TrGCN 结构，从 common sense knowledge graphs 中，学习类别的表示形式，有效地提高了 zero-shot learning 的准确性。