  Although deep convolutional neural networks(CNNs) have achieved remarkable
results on object detection and segmentation, pre- and post-processing steps
such as region proposals and non-maximum suppression(NMS), have been required.
These steps result in high computational complexity and sensitivity to
hyperparameters, e.g. thresholds for NMS. In this work, we propose a novel
end-to-end trainable deep neural network architecture, which consists of
convolutional and recurrent layers, that generates the correct number of object
instances and their bounding boxes (or segmentation masks) given an image,
using only a single network evaluation without any pre- or post-processing
steps. We have tested on detecting digits in multi-digit images synthesized
using MNIST, automatically segmenting digits in these images, and detecting
cars in the KITTI benchmark dataset. The proposed approach outperforms a strong
CNN baseline on the synthesized digits datasets and shows promising results on
KITTI car detection.
