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.
翻译：对象导航任务要求代理商根据视觉信息在未知环境中定位特定对象。 先前, 图形变异被用来暗含探索对象之间的关系。 但是, 由于对象的可见度不同, 很容易引起对象注意的偏差。 因此, 在本文中, 我们提出了一个定向对象注意(DOA) 图表, 指导代理商明确学习对象之间的注意关系, 从而减少对象注意偏差。 特别是, 我们使用 DOA 图, 分别对原始图像的物体特性和不偏向性适应性图像注意( UAOA) 进行公正的适应性对象注意( UAIA) 。 为了区分不同分支的特征, 提议了一个简明的适应性分支能源分布(ABED) 方法。 我们评估了 AI2- Thor 数据集的方法。 与最新( SOTA) 方法相比, 我们的方法报告成功率提高7.4%、 8.1% 和 17.6% 成功率(SR)、 以路径长度加权成功率(SPL) 和以行动效率加权的成功率衡量成功率(SAE) 。
相关内容
微信扫码咨询专知VIP会员