论文标题
变形金刚的注意力传播能否使人们对检测到的对象的不确定性进行见解？
Can Transformer Attention Spread Give Insights Into Uncertainty of Detected and Tracked Objects?
论文作者
论文摘要
在自主驾驶的背景下，变形金刚已被用来执行对象检测和跟踪。这些模型的一个独特特征是，在每个正向通行证中计算注意力权重，尤其是对模型内部的洞察力，尤其是它认为对给定任务有趣的输入数据的一部分。这种带有输入网格的注意力矩阵可用于每个变压器解码器层中的每个检测到的对象。在这项工作中，我们研究了这些注意力重量的分布：它们如何通过解码器层和轨道的寿命改变？它们可以用于推断有关对象的其他信息，例如检测不确定性吗？尤其是在非结构化环境或在训练过程中不常见的环境中，可靠的检测不确定性衡量对于决定该系统是否仍然可以信任至关重要。
Transformers have recently been utilized to perform object detection and tracking in the context of autonomous driving. One unique characteristic of these models is that attention weights are computed in each forward pass, giving insights into the model's interior, in particular, which part of the input data it deemed interesting for the given task. Such an attention matrix with the input grid is available for each detected (or tracked) object in every transformer decoder layer. In this work, we investigate the distribution of these attention weights: How do they change through the decoder layers and through the lifetime of a track? Can they be used to infer additional information about an object, such as a detection uncertainty? Especially in unstructured environments, or environments that were not common during training, a reliable measure of detection uncertainty is crucial to decide whether the system can still be trusted or not.
