论文标题
对象导航的公正定向对象注意图
Unbiased Directed Object Attention Graph for Object Navigation
论文作者
论文摘要
对象导航任务要求代理根据视觉信息在未知环境中找到特定对象。以前，图形卷积被用于隐式探索对象之间的关系。但是，由于对象之间可见性的差异，因此很容易产生对象注意力的偏见。因此，在本文中，我们提出了一个定向的对象注意（DOA）图，以指导代理显式地学习对象之间的注意力关系，从而减少对象的注意偏见。特别是，我们使用DOA图在原始图像上分别对对象特征和无偏的自适应图像注意（UAIA）执行无偏的自适应对象注意（UAOA）。为了区分不同分支的特征，提出了一种简洁的自适应分支分布（ABED）方法。我们在AI2-数据集上评估我们的方法。与最先进的方法（SOTA）方法相比，我们的方法报告了成功率（SR）增长7.4％，8.1％和17.6％，成功按路径长度（SPL）加权（SPL）和按动作效率加权（SAE）。
Object navigation tasks require agents to locate specific objects in unknown environments based on visual information. Previously, graph convolutions were used to implicitly explore the relationships between objects. However, due to differences in visibility among objects, it is easy to generate biases in object attention. Thus, in this paper, we propose a directed object attention (DOA) graph to guide the agent in explicitly learning the attention relationships between objects, thereby reducing the object attention bias. In particular, we use the DOA graph to perform unbiased adaptive object attention (UAOA) on the object features and unbiased adaptive image attention (UAIA) on the raw images, respectively. To distinguish features in different branches, a concise adaptive branch energy distribution (ABED) method is proposed. We assess our methods on the AI2-Thor dataset. Compared with the state-of-the-art (SOTA) method, our method reports 7.4%, 8.1% and 17.6% increase in success rate (SR), success weighted by path length (SPL) and success weighted by action efficiency (SAE), respectively.
