  To predict a set of diverse and informative proposals with enriched
representations, this paper introduces a differentiable Determinantal Point
Process (DPP) layer that is able to augment the object detection architectures.
Most modern object detection architectures, such as Faster R-CNN, learn to
localize objects by minimizing deviations from the ground-truth but ignore
correlation between multiple proposals and object categories. Non-Maximum
Suppression (NMS) as a widely used proposal pruning scheme ignores label- and
instance-level relations between object candidates resulting in multi-labeled
detections. In the multi-class case, NMS selects boxes with the largest
prediction scores ignoring the semantic relation between categories of
potential election. In contrast, our trainable DPP layer, allowing for Learning
Detection with Diverse Proposals (LDDP), considers both label-level contextual
information and spatial layout relationships between proposals without
increasing the number of parameters of the network, and thus improves location
and category specifications of final detected bounding boxes substantially
during both training and inference schemes. Furthermore, we show that LDDP
keeps it superiority over Faster R-CNN even if the number of proposals
generated by LDPP is only ~30% as many as those for Faster R-CNN.
