  The complexity-precision trade-off of an object detector is a critical
problem for resource constrained vision tasks. Previous works have emphasized
detectors implemented with efficient backbones. The impact on this trade-off of
proposal processing by the detection head is investigated in this work. It is
hypothesized that improved detection efficiency requires a paradigm shift,
towards the unequal processing of proposals, assigning more computation to good
proposals than poor ones. This results in better utilization of available
computational budget, enabling higher accuracy for the same FLOPS. We formulate
this as a learning problem where the goal is to assign operators to proposals,
in the detection head, so that the total computational cost is constrained and
the precision is maximized. The key finding is that such matching can be
learned as a function that maps each proposal embedding into a one-hot code
over operators. While this function induces a complex dynamic network routing
mechanism, it can be implemented by a simple MLP and learned end-to-end with
off-the-shelf object detectors. This 'dynamic proposal processing' (DPP) is
shown to outperform state-of-the-art end-to-end object detectors (DETR, Sparse
R-CNN) by a clear margin for a given computational complexity.
