Dynamic Loss Decay based Robust Oriented Object Detection on Remote Sensing Images with Noisy Labels

• URL: http://arxiv.org/abs/2405.09024v1
• Date: Wed, 15 May 2024 01:29:28 GMT
• Title: Dynamic Loss Decay based Robust Oriented Object Detection on Remote Sensing Images with Noisy Labels
• Authors: Guozhang Liu, Ting Liu, Mengke Yuan, Tao Pang, Guangxing Yang, Hao Fu, Tao Wang, Tongkui Liao,
• Abstract summary: We propose a robust oriented remote sensing object detection method through dynamic loss decay (DLD) mechanism. Our solution has won the 2st place in the "fine-grained object detection based on sub-meter remote sensing imagery" track with noisy labels of 2023 National Big Data and Computing Intelligence Challenge.
• Score: 17.211935951030114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ambiguous appearance, tiny scale, and fine-grained classes of objects in remote sensing imagery inevitably lead to the noisy annotations in category labels of detection dataset. However, the effects and treatments of the label noises are underexplored in modern oriented remote sensing object detectors. To address this issue, we propose a robust oriented remote sensing object detection method through dynamic loss decay (DLD) mechanism, inspired by the two phase ``early-learning'' and ``memorization'' learning dynamics of deep neural networks on clean and noisy samples. To be specific, we first observe the end point of early learning phase termed as EL, after which the models begin to memorize the false labels that significantly degrade the detection accuracy. Secondly, under the guidance of the training indicator, the losses of each sample are ranked in descending order, and we adaptively decay the losses of the top K largest ones (bad samples) in the following epochs. Because these large losses are of high confidence to be calculated with wrong labels. Experimental results show that the method achieves excellent noise resistance performance tested on multiple public datasets such as HRSC2016 and DOTA-v1.0/v2.0 with synthetic category label noise. Our solution also has won the 2st place in the "fine-grained object detection based on sub-meter remote sensing imagery" track with noisy labels of 2023 National Big Data and Computing Intelligence Challenge.

Related papers

• Mitigating Noisy Supervision Using Synthetic Samples with Soft Labels [13.314778587751588]
  Noisy labels are ubiquitous in real-world datasets, especially in the large-scale ones derived from crowdsourcing and web searching. It is challenging to train deep neural networks with noisy datasets since the networks are prone to overfitting the noisy labels during training. We propose a framework that trains the model with new synthetic samples to mitigate the impact of noisy labels.
  arXiv  Detail & Related papers  (2024-06-22T04:49:39Z)
• Better Sampling, towards Better End-to-end Small Object Detection [7.7473020808686694]
  Small object detection remains unsatisfactory due to limited characteristics and high density and mutual overlap. We propose methods enhancing sampling within an end-to-end framework. Our model demonstrates a significant enhancement, achieving a 2.9% increase in average precision (AP) over the state-of-the-art (SOTA) on the VisDrone dataset.
  arXiv  Detail & Related papers  (2024-05-17T04:37:44Z)
• Robust Tiny Object Detection in Aerial Images amidst Label Noise [50.257696872021164]
  This study addresses the issue of tiny object detection under noisy label supervision. We propose a DeNoising Tiny Object Detector (DN-TOD), which incorporates a Class-aware Label Correction scheme. Our method can be seamlessly integrated into both one-stage and two-stage object detection pipelines.
  arXiv  Detail & Related papers  (2024-01-16T02:14:33Z)
• Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile [78.1212767880785]
  meta-learner is prone to overfitting since there are only a few available samples. When handling the data with noisy labels, the meta-learner could be extremely sensitive to label noise. We present Eigen-Reptile (ER) that updates the meta- parameters with the main direction of historical task-specific parameters.
  arXiv  Detail & Related papers  (2022-06-04T08:48:02Z)
• Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis [69.48582264712854]
  We propose a robust learning method to perform robust visual sentiment analysis. Our method relies on an external memory to aggregate and filter noisy labels during training. We establish a benchmark for visual sentiment analysis with label noise using publicly available datasets.
  arXiv  Detail & Related papers  (2021-09-15T18:18:28Z)
• Learning from Noisy Labels via Dynamic Loss Thresholding [69.61904305229446]
  We propose a novel method named Dynamic Loss Thresholding (DLT) During the training process, DLT records the loss value of each sample and calculates dynamic loss thresholds. Experiments on CIFAR-10/100 and Clothing1M demonstrate substantial improvements over recent state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-01T07:59:03Z)
• SWIPENET: Object detection in noisy underwater images [41.35601054297707]
  We propose a novel Sample-WeIghted hyPEr Network (SWIPENET), and a robust training paradigm named Curriculum Multi-Class Adaboost (CMA) to address these two problems. The backbone of SWIPENET produces multiple high resolution and semantic-rich Hyper Feature Maps, which significantly improve small object detection. Inspired by the human education process that drives the learning from easy to hard concepts, we here propose the CMA training paradigm that first trains a clean detector which is free from the influence of noisy data.
  arXiv  Detail & Related papers  (2020-10-19T16:41:20Z)
• SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing [131.04304632759033]
  Small and cluttered objects are common in real-world which are challenging for detection. In this paper, we first innovatively introduce the idea of denoising to object detection. Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects.
  arXiv  Detail & Related papers  (2020-04-28T06:03:54Z)
• Solving Missing-Annotation Object Detection with Background Recalibration Loss [49.42997894751021]
  This paper focuses on a novel and challenging detection scenario: A majority of true objects/instances is unlabeled in the datasets. Previous art has proposed to use soft sampling to re-weight the gradients of RoIs based on the overlaps with positive instances, while their method is mainly based on the two-stage detector. In this paper, we introduce a superior solution called Background Recalibration Loss (BRL) that can automatically re-calibrate the loss signals according to the pre-defined IoU threshold and input image.
  arXiv  Detail & Related papers  (2020-02-12T23:11:46Z)

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.