Co-Paced Learning Strategy Based on Confidence for Flying Bird Object Detection Model Training

• URL: http://arxiv.org/abs/2501.12071v1
• Date: Tue, 21 Jan 2025 11:54:37 GMT
• Title: Co-Paced Learning Strategy Based on Confidence for Flying Bird Object Detection Model Training
• Authors: Zi-Wei Sun, Ze-Xi Hua, Heng-Chao Li, Yan Li,
• Abstract summary: Experimental results on two different datasets of flying bird objects in surveillance videos demonstrate that, compared to other model learning strategies, CPL-BC significantly improves detection accuracy.
• Score: 9.597393200515377
• License:
• Abstract: To mitigate the adverse effects of hard samples on the training of the Flying Bird Object Detection (FBOD) model for surveillance videos, we propose a Co-Paced Learning Based on Confidence (CPL-BC) strategy and apply this strategy to the training process of the FBOD model. This strategy involves maintaining two models with identical structures but different initial parameter configurations, which collaborate with each other to select easy samples with prediction confidence exceeding a set threshold for training. As training progresses, the strategy gradually lowers the threshold, allowing more samples to participate, enhancing the model's ability to recognize objects from easy to hard. Before applying the CPL-BC strategy to train the FBOD models, we initially trained the two FBOD models to equip them with the capability to assess the difficulty level of flying bird object samples. Experimental results on two different datasets of flying bird objects in surveillance videos demonstrate that, compared to other model learning strategies, CPL-BC significantly improves detection accuracy, verifying the effectiveness and advancement of this method.

Related papers

• Self-Paced Learning Strategy with Easy Sample Prior Based on Confidence for the Flying Bird Object Detection Model Training [9.597393200515377]
  The Flying Bird Object Detection model (FBOD model) is designed 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) 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.
  arXiv  Detail & Related papers  (2024-12-09T08:53:56Z)
• Towards Robust Federated Learning via Logits Calibration on Non-IID Data [49.286558007937856]
  Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. Recent studies have shown that FL is vulnerable to adversarial examples, leading to a significant drop in its performance. In this work, we adopt the adversarial training (AT) framework to improve the robustness of FL models against adversarial example (AE) attacks.
  arXiv  Detail & Related papers  (2024-03-05T09:18:29Z)
• Robust Training of Federated Models with Extremely Label Deficiency [84.00832527512148]
  Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. We propose a novel twin-model paradigm, called Twin-sight, designed to enhance mutual guidance by providing insights from different perspectives of labeled and unlabeled data. Our comprehensive experiments on four benchmark datasets provide substantial evidence that Twin-sight can significantly outperform state-of-the-art methods across various experimental settings.
  arXiv  Detail & Related papers  (2024-02-22T10:19:34Z)
• Model predictive control-based value estimation for efficient reinforcement learning [6.8237783245324035]
  We design an improved reinforcement learning method based on model predictive control that models the environment through a data-driven approach. Based on the learned environment model, it performs multi-step prediction to estimate the value function and optimize the policy. The method demonstrates higher learning efficiency, faster convergent speed of strategies tending to the local optimal value, and less sample capacity space required by experience replay buffers.
  arXiv  Detail & Related papers  (2023-10-25T13:55:14Z)
• One-stop Training of Multiple Capacity Models [74.87789190840527]
  We propose a novel one-stop training framework to jointly train high-capacity and low-capactiy models. Unlike knowledge distillation, where multiple capacity models are trained from scratch separately, our approach integrates supervisions from different capacity models simultaneously.
  arXiv  Detail & Related papers  (2023-05-23T13:44:09Z)
• A Simple Structure For Building A Robust Model [7.8383976168377725]
  We propose a simple architecture to build a model with a certain degree of robustness, which improves the robustness of the trained network by adding an adversarial sample detection network for cooperative training. We conduct some experiments to test the effectiveness of this design based on Cifar10 dataset, and the results indicate that it has some degree of positive effect on the robustness of the model.
  arXiv  Detail & Related papers  (2022-04-25T12:30:35Z)
• DST: Dynamic Substitute Training for Data-free Black-box Attack [79.61601742693713]
  We propose a novel dynamic substitute training attack method to encourage substitute model to learn better and faster from the target model. We introduce a task-driven graph-based structure information learning constrain to improve the quality of generated training data.
  arXiv  Detail & Related papers  (2022-04-03T02:29:11Z)
• Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic [67.00475077281212]
  Model-based reinforcement learning algorithms are more sample efficient than their model-free counterparts. We propose a novel approach that achieves high sample efficiency without the strong reliance on accurate learned models. We show that CMBAC significantly outperforms state-of-the-art approaches in terms of sample efficiency on several challenging tasks.
  arXiv  Detail & Related papers  (2021-12-16T15:33:11Z)
• Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder [18.375585982984845]
  We focus on enhancing the model's ability to defend gradient-based adversarial attack during the model's training process. We propose two novel adversarial training approaches: CARL and RAR. Experiments show that the proposed two approaches outperform strong baselines on various text classification datasets.
  arXiv  Detail & Related papers  (2021-09-14T09:08:58Z)
• Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models [51.744357472072416]
  We propose a method, which continually identifies the weak spots of a model to generate more valuable training instances. Experimental results show that such an adversarial training method combined with the pretraining strategy can improve both the generalization and robustness of multiple CSC models.
  arXiv  Detail & Related papers  (2021-05-31T09:17:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.