The ventral visual stream (VVS) is a fundamental pathway involved in visual object identification and recognition. In this work, we present a hypothesis of a sequence of computations performed by the VVS during object recognition. The operations performed by the inferior temporal (IT) cortex are represented as not being akin to a neural-network, but rather in-line with a dynamic inference instantiation of the untangling notion. The presentation draws upon a technique for dynamic maximum a posteriori probability (MAP) sequence estimation based on the Viterbi algorithm. Simulation results are presented to show that the decoding portion of the architecture that is associated with the IT can effectively untangle object identity when presented with synthetic data. More importantly, we take a step forward in visual neuroscience by presenting a framework for an inference-based approach that is biologically inspired via attributes implicated in primate object recognition. The analysis will provide insight in explaining the exceptional proficiency of the VVS.

中文翻译：

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通过动态推理在腹侧视觉流的较高区域进行对象识别

腹侧视觉流 (VVS) 是参与视觉对象识别和识别的基本途径。在这项工作中，我们提出了 VVS 在对象识别期间执行的一系列计算的假设。由下颞 (IT) 皮层执行的操作被表示为不类似于神经网络，而是与解开概念的动态推理实例化一致。该演示文稿利用了一种基于维特比算法的动态最大后验概率 (MAP) 序列估计技术。仿真结果表明，与 IT 相关联的架构的解码部分在呈现合成数据时可以有效地解开对象身份。更重要的是，我们通过提出一个基于推理的方法框架，在视觉神经科学方面向前迈进了一步，该框架通过灵长类动物物体识别中涉及的属性受到生物学启发。该分析将为解释 VVS 的卓越能力提供洞察力。