  The recent COCO object detection dataset presents several new challenges for
object detection. In particular, it contains objects at a broad range of
scales, less prototypical images, and requires more precise localization. To
address these challenges, we test three modifications to the standard Fast
R-CNN object detector: (1) skip connections that give the detector access to
features at multiple network layers, (2) a foveal structure to exploit object
context at multiple object resolutions, and (3) an integral loss function and
corresponding network adjustment that improve localization. The result of these
modifications is that information can flow along multiple paths in our network,
including through features from multiple network layers and from multiple
object views. We refer to our modified classifier as a "MultiPath" network. We
couple our MultiPath network with DeepMask object proposals, which are well
suited for localization and small objects, and adapt our pipeline to predict
segmentation masks in addition to bounding boxes. The combined system improves
results over the baseline Fast R-CNN detector with Selective Search by 66%
overall and by 4x on small objects. It placed second in both the COCO 2015
detection and segmentation challenges.
