Unsupervised Hard Example Mining from Videos for Improved Object Detection

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Unsupervised Hard Example Mining from Videos for Improved Object Detection ,

SouYoung Jin*, Aruni RoyChowdhury*, Huaizu Jiang, Ashish Singh, Aditya Prasad, Deep Chakraborty and Erik Learned-Miller

European Conference on Computer Vision (ECCV), 2018

[paper] [supp-pdf] [supp-video] [arxiv] [bibtex]

, SouYoung Jin*, Aruni RoyChowdhury*, Huaizu Jiang, Ashish Singh, Aditya Prasad, Deep Chakraborty and Erik Learned-Miller European Conference on Computer Vision (ECCV), 2018 [paper] [supp-pdf] [supp-video] [arxiv] [bibtex] NEW Follow-up work on domain adaptation at CVPR 2019

Abstract

Extension to COCO classes

Acknowledgements

Important gains have recently been obtained in object detection by using training objectives that focus on hard negative examples, i.e., negative examples that are currently rated as positive or ambiguous by the detector. These examples can strongly influence parameters when the network is trained to correct them. Unfortunately, they are often sparse in the training data, and are expensive to obtain. In this work, we show how large numbers of hard negatives can be obtained automatically by analyzing the output of a trained detector on video sequences. In particular, detections that are isolated in time, i.e., that have no associated preceding or following detections, are likely to be hard negatives. We describe simple procedures for mining large numbers of such hard negatives (and also hard positives) from unlabeled video data. Our experiments show that retraining detectors on these automatically obtained examples often significantly improves performance. We present experiments on multiple architectures and multiple data sets, including face detection, pedestrian detection and other object categories. Code and results for extending hard example mining to arbitrary classes from MS-COCO.This research is based in part upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) under contract number 2014-14071600010 and in part on research sponsored by the Air Force Research Laboratory and DARPA under agreement number FA8750-18-2-0126. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, the Air Force Research Laboratory and DARPA or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon. The experiments were performed using high performance computing equipment obtained under a grant from the Collaborative R&D Fund managed by the Massachusetts Tech Collaborative and GPUs donated by NVIDIA.