# Self-Paced Learning Strategy with Easy Sample Prior Based on Confidence for the Flying Bird Object Detection Model Training

arXiv: https://arxiv.org/abs/2412.06306 · HTML: https://mlsanity.com/paper/2412.06306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 19 of 19)

- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [Automated Curriculum Learning for Neural Networks](https://mlsanity.com/paper/1704.03003/) (2017)
- [Decoupling "when to update" from "how to update"](https://mlsanity.com/paper/1706.02613/) (2017)
- [Teacher-Student Curriculum Learning](https://mlsanity.com/paper/1707.00183/) (2017)
- [Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples](https://mlsanity.com/paper/1704.07433/) (2017)
- [Gradient Harmonized Single-stage Detector](https://mlsanity.com/paper/1811.05181/) (2018)
- [Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation](https://mlsanity.com/paper/2005.03572/) (2020)
- [Webly Supervised Learning of Convolutional Networks](https://mlsanity.com/paper/1505.01554/) (2015)
- [STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation](https://mlsanity.com/paper/1509.03150/) (2015)
- [How hard can it be? Estimating the difficulty of visual search in an image](https://mlsanity.com/paper/1705.08280/) (2017)
- [Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)](https://mlsanity.com/paper/1910.08967/) (2019)
- [Self-Paced Learning: an Implicit Regularization Perspective](https://mlsanity.com/paper/1606.00128/) (2016)
- [Flying Bird Object Detection Algorithm in Surveillance Video Based on Motion Information](https://mlsanity.com/paper/2301.01917/) (2023)
- [FBD-SV-2024: Flying Bird Object Detection Dataset in Surveillance Video](https://mlsanity.com/paper/2409.00317/) (2024)
- [A Flying Bird Object Detection Method for Surveillance Video](https://mlsanity.com/paper/2401.03749/) (2024)

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

- [Co-Paced Learning Strategy Based on Confidence for Flying Bird Object Detection Model Training](https://mlsanity.com/paper/2501.12071/) (2025)

## Abstract (arXiv)

In order to avoid the impact of hard samples on the training process of the Flying Bird Object Detection model (FBOD model, in our previous work, we designed the FBOD model according to the characteristics of flying bird objects in surveillance video), the Self-Paced Learning strategy with Easy Sample Prior Based on Confidence (SPL-ESP-BC), a new model training strategy, is proposed. Firstly, the loss-based Minimizer Function in Self-Paced Learning (SPL) is improved, and the confidence-based Minimizer Function is proposed, which makes it more suitable for one-class object detection tasks. Secondly, to give the model the ability to judge easy and hard samples at the early stage of training by using the SPL strategy, an SPL strategy with Easy Sample Prior (ESP) is proposed. The FBOD model is trained using the standard training strategy with easy samples first, then the SPL strategy with all samples is used to train it. Combining the strategy of the ESP and the Minimizer Function based on confidence, the SPL-ESP-BC model training strategy is proposed. Using this strategy to train the FBOD model can make it to learn the characteristics of the flying bird object in the surveillance video better, from easy to hard. The experimental results show that compared with the standard training strategy that does not distinguish between easy and hard samples, the AP50 of the FBOD model trained by the SPL-ESP-BC is increased by 2.1%, and compared with other loss-based SPL strategies, the FBOD model trained with SPL-ESP-BC strategy has the best comprehensive detection performance.