  In this report, we describe the technical details of oursubmission to the
EPIC-Kitchens Object Detection Challenge.Duck filling and mix-up techniques are
firstly introduced to augment the data and significantly improve the robustness
of the proposed method. Then we propose GRE-FPN and Hard IoU-imbalance Sampler
methods to extract more representative global object features. To bridge the
gap of category imbalance, Class Balance Sampling is utilized and greatly
improves the test results. Besides, some training and testing strategies are
also exploited, such as Stochastic Weight Averaging and multi-scale testing.
Experimental results demonstrate that our approach can significantly improve
the mean Average Precision (mAP) of object detection on both the seen and
unseen test sets of EPICKitchens.
