# Soft-labeling Strategies for Rapid Sub-Typing

arXiv: https://arxiv.org/abs/2209.12684 · HTML: https://mlsanity.com/paper/2209.12684/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 23 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Rethinking Pre-training and Self-training](https://mlsanity.com/paper/2006.06882/) (2020)
- [End-to-End Semi-Supervised Object Detection with Soft Teacher](https://mlsanity.com/paper/2106.09018/) (2021)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [Humble Teachers Teach Better Students for Semi-Supervised Object Detection](https://mlsanity.com/paper/2106.10456/) (2021)
- [Delving Deep into Label Smoothing](https://mlsanity.com/paper/2011.12562/) (2020)
- [Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels](https://mlsanity.com/paper/2005.08455/) (2020)
- [BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training](https://mlsanity.com/paper/2203.13249/) (2022)
- [Towards Noise-resistant Object Detection with Noisy Annotations](https://mlsanity.com/paper/2003.01285/) (2020)
- [Soft Sampling for Robust Object Detection](https://mlsanity.com/paper/1806.06986/) (2018)
- [A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networks](https://mlsanity.com/paper/2007.00800/) (2020)
- [Cheaper Pre-training Lunch: An Efficient Paradigm for Object Detection](https://mlsanity.com/paper/2004.12178/) (2020)

## Built on by (most cited first, 1 of 1)

- [Soft Labels for Rapid Satellite Object Detection](https://mlsanity.com/paper/2212.00585/) (2022)

## Abstract (arXiv)

The challenge of labeling large example datasets for computer vision continues to limit the availability and scope of image repositories. This research provides a new method for automated data collection, curation, labeling, and iterative training with minimal human intervention for the case of overhead satellite imagery and object detection. The new operational scale effectively scanned an entire city (68 square miles) in grid search and yielded a prediction of car color from space observations. A partially trained yolov5 model served as an initial inference seed to output further, more refined model predictions in iterative cycles. Soft labeling here refers to accepting label noise as a potentially valuable augmentation to reduce overfitting and enhance generalized predictions to previously unseen test data. The approach takes advantage of a real-world instance where a cropped image of a car can automatically receive sub-type information as white or colorful from pixel values alone, thus completing an end-to-end pipeline without overdependence on human labor.