基于侧抑制的卷积神经网络的视觉注意和显著性检测
Reproduction of Lateral Inhibition-Inspired Convolutional Neural Network for Visual Attention and Saliency Detection
摘要：
近年来，神经网络持续蓬勃发展，在检测照片中的相关物体或简单地识别（分类）这些物体方面实现了高效率-主要使用CNN网络。然而，目前的解决方案远非理想，因为事实证明，网络甚至可以与自然图像示例相混淆。我怀疑物体的分类受到物体所在的背景像素的强烈影响。在我的工作中，我使用LICNN网络创建的显着图来分析上述问题。它们被设计为抑制被检查对象周围的神经元，从而减少背景像素对分类器预测的贡献。我对自然图像和对抗图像数据集的实验表明，背景和错误分类的前景对象之间确实存在明显的相关性。网络的这种行为并没有得到人类经验的支持，因为，例如，我们不会仅仅因为黄色校车在雪地背景上就把它和扫雪机混淆。
英文摘要：
In recent years, neural networks have continued to flourish, achieving high efficiency in detecting relevant objects in photos or simply recognizing (classifying) these objects - mainly using CNN networks. Current solutions, however, are far from ideal, because it often turns out that network can be effectively confused with even natural images examples. I suspect that the classification of an object is strongly influenced by the background pixels on which the object is located. In my work, I analyze the above problem using for this purpose saliency maps created by the LICNN network. They are designed to suppress the neurons surrounding the examined object and, consequently, reduce the contribution of background pixels to the classifier predictions. My experiments on the natural and adversarial images datasets show that, indeed, there is a visible correlation between the background and the wrong-classified foreground object. This behavior of the network is not supported by human experience, because, for example, we do not confuse the yellow school bus with the snow plow just because it is on the snowy background.
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。
