Due to its ability to integrate information over large boxes, the new layer facilitates long-range propagation of information and leads to the efficient increase ofthe receptivefields ofnetwork units.

Box filters computed using integral images have been part of the computer vision toolset for a long time. Here, we show that a convolutional layer that computes box filter responses in a sliding manner can be used within deep architectures, whereas the dimensions and the offsets of the sliding boxes in such a layer can be learned as part of an end-to-end loss minimization. Crucially, the training process can make the size of the boxes in such a layer arbitrarily large without incurring extra computational cost and without the need to increase the number of learnable parameters. Due to its ability to integrate information over large boxes, the new layer facilitates long-range propagation of information and leads to the efficient increase of the receptive fields of downstream units in the network. By incorporating the new layer into existing architectures for semantic segmentation, we are able to achieve both the increase in segmentation accuracy as well as the decrease in the computational cost and the number of learnable parameters.

Due to its ability to integrate information over large boxes, the new layer facilitates long-range propagation of information and leads to the efficient increase of the receptive fields of network units.

Thank you for the clarifications. I am inclined to keep my ratings. One concern I still have is the new approach (BoxERFNet) doesn't seem to be giving any gains in Cityscapes (test) so I not sure how effective the approach is. The results on the SUN RGBD are good but as mentioned, the earlier models were not tuned for that dataset, so it is hard to make a strong conclusion. Integral images used a trick for fast computation and has been used for many problems in computer vision but had not been used in neural networks yet.

Box filters computed using integral images have been part of the computer vision toolset for a long time. Here, we show that a convolutional layer that computes box filter responses in a sliding manner can be used within deep architectures, whereas the dimensions and the offsets of the sliding boxes in such a layer can be learned as part of an end-to-end loss minimization... Crucially, the training process can make the size of the boxes in such a layer arbitrarily large without incurring extra computational cost and without the need to increase the number of learnable parameters. Due to its ability to integrate information over large boxes, the new layer facilitates long-range propagation of information and leads to the efficient increase of the receptive fields of downstream units in the network. By incorporating the new layer into existing architectures for semantic segmentation, we are able to achieve both the increase in segmentation accuracy as well as the decrease in the computational cost and the number of learnable parameters.

Box filters computed using integral images have been part of the computer vision toolset for a long time. Here, we show that a convolutional layer that computes box filter responses in a sliding manner can be used within deep architectures, whereas the dimensions and the offsets of the sliding boxes in such a layer can be learned as part of an end-to-end loss minimization. Crucially, the training process can make the size of the boxes in such a layer arbitrarily large without incurring extra computational cost and without the need to increase the number of learnable parameters. Due to its ability to integrate information over large boxes, the new layer facilitates long-range propagation of information and leads to the efficient increase of the receptive fields of downstream units in the network. By incorporating the new layer into existing architectures for semantic segmentation, we are able to achieve both the increase in segmentation accuracy as well as the decrease in the computational cost and the number of learnable parameters.

Box filters computed using integral images have been part of the computer vision toolset for a long time. Here, we show that a convolutional layer that computes box filter responses in a sliding manner can be used within deep architectures, whereas the dimensions and the offsets of the sliding boxes in such a layer can be learned as part of an end-to-end loss minimization. Crucially, the training process can make the size of the boxes in such a layer arbitrarily large without incurring extra computational cost and without the need to increase the number of learnable parameters. Due to its ability to integrate information over large boxes, the new layer facilitates long-range propagation of information and leads to the efficient increase of the receptive fields of downstream units in the network. By incorporating the new layer into existing architectures for semantic segmentation, we are able to achieve both the increase in segmentation accuracy as well as the decrease in the computational cost and the number of learnable parameters.

Box filters computed using integral images have been part of the computer vision toolset for a long time. Here, we show that a convolutional layer that computes box filter responses in a sliding manner can be used within deep architectures, whereas the dimensions and the offsets of the sliding boxes in such a layer can be learned as part of an end-to-end loss minimization. Crucially, the training process can make the size of the boxes in such a layer arbitrarily large without incurring extra computational cost and without the need to increase the number of learnable parameters. Due to its ability to integrate information over large boxes, the new layer facilitates long-range propagation of information and leads to the efficient increase of the receptive fields of downstream units in the network. By incorporating the new layer into existing architectures for semantic segmentation, we are able to achieve both the increase in segmentation accuracy as well as the decrease in the computational cost and the number of learnable parameters.