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.
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,
Code and Results
Extension to COCO classes
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.