# Semi-Supervised Object Detection with Sparsely Annotated Dataset

arXiv: https://arxiv.org/abs/2006.11692 · HTML: https://mlsanity.com/paper/2006.11692/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining](https://mlsanity.com/paper/2201.04620/) (2022)
- [A Comparative Review of Recent Few-Shot Object Detection Algorithms](https://mlsanity.com/paper/2111.00201/) (2021)
- [Scaling up instance annotation via label propagation](https://mlsanity.com/paper/2110.02277/) (2021)
- [AVCAffe: A Large Scale Audio-Visual Dataset of Cognitive Load and Affect for Remote Work](https://mlsanity.com/paper/2205.06887/) (2022)
- [Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection](https://mlsanity.com/paper/2312.13822/) (2023)
- [FD-SOS: Vision-Language Open-Set Detectors for Bone Fenestration and Dehiscence Detection from Intraoral Images](https://mlsanity.com/paper/2407.09088/) (2024)
- [Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Picking](https://mlsanity.com/paper/2403.02495/) (2024)
- [Semi-supervised Source Detection in Astronomical Images: New Benchmark and Strong Baseline](https://mlsanity.com/paper/2606.09219/) (2026)
- [Supervised Virtual-to-Real Domain Adaptation for Object Detection Task using YOLO](https://mlsanity.com/paper/2302.13891/) (2023)
- [Towards Sparsely Annotated Open-World Object Detection](https://mlsanity.com/paper/2608.12714/) (2026)

## Abstract (arXiv)

In training object detector based on convolutional neural networks, selection of effective positive examples for training is an important factor. However, when training an anchor-based detectors with sparse annotations on an image, effort to find effective positive examples can hinder training performance. When using the anchor-based training for the ground truth bounding box to collect positive examples under given IoU, it is often possible to include objects from other classes in the current training class, or objects that are needed to be trained can only be sampled as negative examples. We used two approaches to solve this problem: 1) the use of an anchorless object detector and 2) a semi-supervised learning-based object detection using a single object tracker. The proposed technique performs single object tracking by using the sparsely annotated bounding box as an anchor in the temporal domain for successive frames. From the tracking results, dense annotations for training images were generated in an automated manner and used for training the object detector. We applied the proposed single object tracking-based semi-supervised learning to the Epic-Kitchens dataset. As a result, we were able to achieve runner-up performance in the Unseen section while achieving the first place in the Seen section of the Epic-Kitchens 2020 object detection challenge under IoU > 0.5 evaluation