用于一般目标识别的扫视视觉Transformer
A Saccaded Visual Transformer for General Object Spotting
摘要：
本文提出了一种新颖的组合视觉Transformer风格的补丁分类器与扫视局部注意。还提出了一种用于训练对象模型的新的优化范例，而不是最小化类成员概率误差的优化函数，网络被训练以估计到标记对象的质心的归一化距离。这种方法直接在模型中建立了一定程度的跨国不变性，并允许快速扫视搜索与梯度上升，以找到对象的质心。所得到的扫视视觉Transformer在人脸上演示。
英文摘要：
This paper presents the novel combination of a visual transformer style patch classifier with saccaded local attention. A novel optimisation paradigm for training object models is also presented, rather than the optimisation function minimising class membership probability error the network is trained to estimate the normalised distance to the centroid of labelled objects. This approach builds a degree of transnational invariance directly into the model and allows fast saccaded search with gradient ascent to find object centroids. The resulting saccaded visual transformer is demonstrated on human faces.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
