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Semi-Supervised Object Detection with Sparsely Annotated Dataset

Citation Author(s):
Jihun Yoon,Seungbum Hong,Min-Kook Choi
Submitted by:
Jihun Yoon
Last updated:
25 November 2021 - 3:40am
Document Type:
Research Manuscript
Document Year:
Event:
Presenters Name:
Jihun Yoon
Paper Code:
MLR-APPL-IP-3.11

Abstract 

Abstract: 

When training an anchor-based object detector with a sparsely annotated dataset, the effort required to locate positive examples can cause performance degradation. Because anchor-based object detection models collect positive examples under IoU between anchors and ground-truth bounding boxes, in a sparsely annotated image, some objects that are not annotated can be assigned as negative examples, such as backgrounds. We attempt to solve this problem with two approaches: 1) using an anchor-less object detector and 2) using a single-object tracker for semi-supervised learning-based object detection. The proposed technique performs bidirectional single-object tracking from sparsely annotated bounding boxes as starting points in videos to obtain dense annotations. On applying our method to the EPIC-KITCHENS-55 dataset, we were able to achieve runner-up performance in the Unseen section, while achieving the first place in the Seen section of the EPICKITCHENS 2020 object detection challenge under IoU > 0.5 on the EPIC-KITCHENS 2020 object detection challenge.

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Dataset Files

ICIP2021 paper - SEMI-SUPERVISED OBJECT DETECTION WITH SPARSELY ANNOTATED DATASET.pdf

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