# DHARI Report to EPIC-Kitchens 2020 Object Detection Challenge

Source: [DHARI Report to EPIC-Kitchens 2020 Object Detection Challenge](<https://www.sota2.com/research/paper/68fa67d41e209131cea18c35>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:24:01.852Z

Authors: Kaide Li, Bingyan Liao, Laifeng Hu, Yaonong Wang

Published: 2020-06-28T09:29:48.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2006.15553>)

[PDF](<https://arxiv.org/pdf/2006.15553.pdf>)