Robustness to variations in lighting conditions is a key objective for any deep vision system. To this end, our paper extends the receptive field of convolutional neural networks with two residual components, ubiquitous in the visual processing system of vertebrates: On-center and off-center pathways, with excitatory center and inhibitory surround; OOCS for short. The on-center pathway is excited by the presence of a light stimulus in its center but not in its surround, whereas the off-center one is excited by the absence of a light stimulus in its center but not in its surround. We design OOCS pathways via a difference of Gaussians, with their variance computed analytically from the size of the receptive fields. OOCS pathways complement each other in their response to light stimuli, ensuring this way a strong edge-detection capability, and as a result, an accurate and robust inference under challenging lighting conditions. We provide extensive empirical evidence showing that networks supplied with the OOCS edge representation gain accuracy and illumination-robustness compared to standard deep models.
翻译:灯光条件变化的强力是任何深视系统的关键目标。 为此,我们的文件扩展了具有两个剩余组成部分的进化神经网络的可接受领域,其中有两个剩余组成部分,在脊椎动物的视觉处理系统中无处不在:中心和非中心路径,有刺激的中心和抑制性环绕;短期的 OOCS 。 中心路径由于中心有光刺激,但周围没有光刺激,而中心外部分却因中心没有光刺激而兴奋,而中心外部分则因中心没有光刺激而感到兴奋。 我们设计OOCS 路径,通过高斯人的差异来设计OOCS 路径,这些路径在分析上与接受场的大小有差异。 OOCS 路径在对光刺激的反应中互相补充,确保以这种方式获得强烈的边缘探测能力,从而在充满挑战的照明条件下准确和有力的推断。 我们提供了广泛的经验证据,表明提供OOCS边缘代表网络的网络与标准的深层模型相比,其准确性和破坏力。