In this work we introduce a biologically inspired long-range skip connection for the UNet architecture that relies on the perceptual illusion of hybrid images, being images that simultaneously encode two images. The fusion of early encoder features with deeper decoder ones allows UNet models to produce finer-grained dense predictions. While proven in segmentation tasks, the network's benefits are down-weighted for dense regression tasks as these long-range skip connections additionally result in texture transfer artifacts. Specifically for depth estimation, this hurts smoothness and introduces false positive edges which are detrimental to the task due to the depth maps' piece-wise smooth nature. The proposed HybridSkip connections show improved performance in balancing the trade-off between edge preservation, and the minimization of texture transfer artifacts that hurt smoothness. This is achieved by the proper and balanced exchange of information that Hybrid-Skip connections offer between the high and low frequency, encoder and decoder features, respectively.
翻译:在这项工作中,我们为UNet 结构引入了一种由生物启发的远程跳过连接,这种连接依赖于混合图像的感知错觉,即同时编码两种图像的图像。早期编码器特性与更深的解码器特性结合后,UNet 模型可以产生精细的密度预测。虽然在分解任务中证明了网络的好处,但对于密度回归任务而言,这些长距离跳过连接的好处是低比加权的,因为这些长距离跳过会增加纹理传输工艺。具体地说,为了深度估计,这伤害了光滑度,并引入了虚假的正边缘,而由于深度地图的精密光滑性而不利于任务。提议的混合键连接显示,在平衡边缘保护与妨碍光滑的质转移工艺之间的交易和尽量减少之间,取得了更好的业绩。这是通过混合键连接在高频和低频、编码器和分解码特性之间进行适当和平衡的信息交流而实现的。