CCML: A Novel Collaborative Learning Model for Classification of Remote Sensing Images with Noisy Multi-Labels

• URL: http://arxiv.org/abs/2012.10715v2
• Date: Sun, 27 Dec 2020 10:15:06 GMT
• Title: CCML: A Novel Collaborative Learning Model for Classification of Remote Sensing Images with Noisy Multi-Labels
• Authors: Ahmet Kerem Aksoy, Mahdyar Ravanbakhsh, Tristan Kreuziger, Begum Demir
• Abstract summary: We propose a novel Consensual Collaborative Multi-Label Learning (CCML) method to alleviate the adverse effects of multi-label noise during the training phase of the CNN model. CCML identifies, ranks, and corrects noisy multi-labels in RS images based on four main modules.
• Score: 0.9995347522610671
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. Deep Convolutional Neural Networks (CNNs) based methods have triggered substantial performance gains in RS MLC problems, requiring a large number of reliable training images annotated by multiple land-cover class labels. Collecting such data is time-consuming and costly. To address this problem, the publicly available thematic products, which can include noisy labels, can be used for annotating RS images with zero-labeling cost. However, multi-label noise (which can be associated with wrong as well as missing label annotations) can distort the learning process of the MLC algorithm, resulting in inaccurate predictions. The detection and correction of label noise are challenging tasks, especially in a multi-label scenario, where each image can be associated with more than one label. To address this problem, we propose a novel Consensual Collaborative Multi-Label Learning (CCML) method to alleviate the adverse effects of multi-label noise during the training phase of the CNN model. CCML identifies, ranks, and corrects noisy multi-labels in RS images based on four main modules: 1) group lasso module; 2) discrepancy module; 3) flipping module; and 4) swap module. The task of the group lasso module is to detect the potentially noisy labels assigned to the multi-labeled training images, and the discrepancy module ensures that the two collaborative networks learn diverse features, while obtaining the same predictions. The flipping module is designed to correct the identified noisy multi-labels, while the swap module task is devoted to exchanging the ranking information between two networks. Our code is publicly available online: http://www.noisy-labels-in-rs.org

Related papers

• Multi-Label Self-Supervised Learning with Scene Images [21.549234013998255]
  This paper shows that quality image representations can be learned by treating scene/multi-label image SSL simply as a multi-label classification problem. The proposed method is named Multi-Label Self-supervised learning (MLS)
  arXiv  Detail & Related papers  (2023-08-07T04:04:22Z)
• Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation [75.32213865436442]
  We propose an end-to-end multi-granularity denoising and bidirectional alignment (MDBA) model to alleviate the noisy label and multi-class generalization issues. The MDBA model can reach the mIoU of 69.5% and 70.2% on validation and test sets for the PASCAL VOC 2012 dataset.
  arXiv  Detail & Related papers  (2023-05-09T03:33:43Z)
• Category-Adaptive Label Discovery and Noise Rejection for Multi-label Image Recognition with Partial Positive Labels [78.88007892742438]
  Training multi-label models with partial positive labels (MLR-PPL) attracts increasing attention. Previous works regard unknown labels as negative and adopt traditional MLR algorithms. We propose to explore semantic correlation among different images to facilitate the MLR-PPL task.
  arXiv  Detail & Related papers  (2022-11-15T02:11:20Z)
• On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification [1.6758573326215689]
  The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The use of deep neural networks that require a high number of reliable training images annotated by multiple land-cover class labels (multi-labels) have been found popular in RS. In this paper, we investigate three different noise robust CV SLC methods and adapt them to be robust for multi-label noise scenarios in RS.
  arXiv  Detail & Related papers  (2022-07-28T09:38:30Z)
• Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation [119.009033745244]
  This paper presents a Self-supervised Low-Rank Network ( SLRNet) for single-stage weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS) SLRNet uses cross-view self-supervision, that is, it simultaneously predicts several attentive LR representations from different views of an image to learn precise pseudo-labels. Experiments on the Pascal VOC 2012, COCO, and L2ID datasets demonstrate that our SLRNet outperforms both state-of-the-art WSSS and SSSS methods with a variety of different settings.
  arXiv  Detail & Related papers  (2022-03-19T09:19:55Z)
• Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels [86.17081952197788]
  We propose to blend category-specific representation across different images to transfer information of known labels to complement unknown labels. Experiments on the MS-COCO, Visual Genome, Pascal VOC 2007 datasets show that the proposed SARB framework obtains superior performance over current leading competitors.
  arXiv  Detail & Related papers  (2022-03-04T07:56:16Z)
• Structured Semantic Transfer for Multi-Label Recognition with Partial Labels [85.6967666661044]
  We propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels. The framework consists of two complementary transfer modules that explore within-image and cross-image semantic correlations. Experiments on the Microsoft COCO, Visual Genome and Pascal VOC datasets show that the proposed SST framework obtains superior performance over current state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2021-12-21T02:15:01Z)
• Multi-label Iterated Learning for Image Classification with Label Ambiguity [3.5736176624479654]
  We propose multi-label iterated learning (MILe) to incorporate the inductive biases of multi-label learning from single labels. MILe is a simple yet effective procedure that builds a multi-label description of the image by propagating binary predictions. We show that MILe is effective reducing label noise, achieving state-of-the-art performance on real-world large-scale noisy data such as WebVision.
  arXiv  Detail & Related papers  (2021-11-23T22:10:00Z)
• A Novel Uncertainty-aware Collaborative Learning Method for Remote Sensing Image Classification Under Multi-Label Noise [0.9995347522610671]
  This paper proposes an architect-independent Consensual Collaborative Multi-Label Learning (CCML) method to train deep classifiers. The CCML identifies, ranks, and corrects noisy multi-label images through four main modules. Experiments conducted on the multi-label RS image archive IR-BigEarthNet confirm the robustness of the proposed CCML.
  arXiv  Detail & Related papers  (2021-05-12T08:06:14Z)
• Attention-Aware Noisy Label Learning for Image Classification [97.26664962498887]
  Deep convolutional neural networks (CNNs) learned on large-scale labeled samples have achieved remarkable progress in computer vision. The cheapest way to obtain a large body of labeled visual data is to crawl from websites with user-supplied labels, such as Flickr. This paper proposes the attention-aware noisy label learning approach to improve the discriminative capability of the network trained on datasets with potential label noise.
  arXiv  Detail & Related papers  (2020-09-30T15:45:36Z)

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.