The partially occluded image recognition (POIR) problem has been a challenge for artificial intelligence for a long time. A common strategy to handle the POIR problem is using the non-occluded features for classification. Unfortunately, this strategy will lose effectiveness when the image is severely occluded, since the visible parts can only provide limited information. Several studies in neuroscience reveal that feature restoration which fills in the occluded information and is called amodal completion is essential for human brains to recognize partially occluded images. However, feature restoration is commonly ignored by CNNs, which may be the reason why CNNs are ineffective for the POIR problem. Inspired by this, we propose a novel brain-inspired feature restoration network (BIFRNet) to solve the POIR problem. It mimics a ventral visual pathway to extract image features and a dorsal visual pathway to distinguish occluded and visible image regions. In addition, it also uses a knowledge module to store object prior knowledge and uses a completion module to restore occluded features based on visible features and prior knowledge. Thorough experiments on synthetic and real-world occluded image datasets show that BIFRNet outperforms the existing methods in solving the POIR problem. Especially for severely occluded images, BIRFRNet surpasses other methods by a large margin and is close to the human brain performance. Furthermore, the brain-inspired design makes BIFRNet more interpretable.

翻译：部分隐蔽的图像识别( POIR) 问题长期以来一直是人工智能的一个挑战。 处理 POIR 问题的通用策略正在使用非隐蔽的分类特性。 不幸的是, 该战略在图像严重隐蔽时将失去效力, 因为可见部分只能提供有限的信息。 神经科学的几项研究显示, 功能恢复是填补隐蔽信息并被称为“ 模式完成” 的功能, 对人类大脑识别部分隐蔽的图像至关重要。 然而, CNN 通常忽视特征恢复, 这可能是CNN对POIR 问题无效的原因。 受此启发, 我们提议建立一个新的由大脑启发的功能恢复网络( BIFRNet ) 网络来解决 POIR 问题。 它模仿一种提取隐蔽信息特征的直视路径和辨辨别隐蔽和可见图像区域。 此外, 它还使用知识模块存储目标先前知识, 并使用一个基于可见特征和先前知识的完成模块来恢复隐蔽的特征。 在合成和真实的 OFRI 网络上进行新的大脑测试, 更深入的图像解析、 以更深层的BIFRL 的当前图像解 的图像解方式展示其他的图像 。</s>