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Objects may appear at arbitrary scales in perspective images of a scene,posing a challenge for recognition systems that process images at a fixedresolution. We propose a depth-aware gating module that adaptively selects thepooling field size in a convolutional network architecture according to theobject scale (inversely proportional to the depth) so that small details arepreserved for distant objects while larger receptive fields are used for thosenearby. The depth gating signal is provided by stereo disparity or estimateddirectly from monocular input. We integrate this depth-aware gating into arecurrent convolutional neural network to perform semantic segmentation. Ourrecurrent module iteratively refines the segmentation results, leveraging thedepth and semantic predictions from the previous iterations.  Through extensive experiments on four popular large-scale RGB-D datasets, wedemonstrate this approach achieves competitive semantic segmentationperformance with a model which is substantially more compact. We carry outextensive analysis of this architecture including variants that operate onmonocular RGB but use depth as side-information during training, unsupervisedgating as a generic attentional mechanism, and multi-resolution gating. We findthat gated pooling for joint semantic segmentation and depth yieldsstate-of-the-art results for quantitative monocular depth estimation.

Recurrent Scene Parsing with Perspective Understanding in the Loop