SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images

• URL: http://arxiv.org/abs/2303.04737v1
• Date: Wed, 8 Mar 2023 17:23:18 GMT
• Title: SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images
• Authors: Yuqun Yang, Xu Tang, Xiangrong Zhang, Jingjing Ma, Licheng Jiao
• Abstract summary: General change detection (GCD) and semantic change detection (SCD) are common methods for identifying changes and distinguishing object categories involved in those changes, respectively. We propose a novel solution that intuitively dividing changes into three trends (appear'', disappear'' and transform'') instead of semantic categories, named it trend change detection (TCD) in this paper. It offers more detailed change information than GCD, while requiring less manual annotation cost than SCD.
• Score: 45.138953422578574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: General change detection (GCD) and semantic change detection (SCD) are common methods for identifying changes and distinguishing object categories involved in those changes, respectively. However, the binary changes provided by GCD is often not practical enough, while annotating semantic labels for training SCD models is very expensive. Therefore, there is a novel solution that intuitively dividing changes into three trends (``appear'', ``disappear'' and ``transform'') instead of semantic categories, named it trend change detection (TCD) in this paper. It offers more detailed change information than GCD, while requiring less manual annotation cost than SCD. However, there are limited public data sets with specific trend labels to support TCD application. To address this issue, we propose a softmatch distance which is used to construct a weakly-supervised TCD branch in a simple GCD model, using GCD labels instead of TCD label for training. Furthermore, a strategic approach is presented to successfully explore and extract background information, which is crucial for the weakly-supervised TCD task. The experiment results on four public data sets are highly encouraging, which demonstrates the effectiveness of our proposed model.

Related papers

• Happy: A Debiased Learning Framework for Continual Generalized Category Discovery [54.54153155039062]
  This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD) C-GCD aims to incrementally discover new classes from unlabeled data while maintaining the ability to recognize previously learned classes. We introduce a debiased learning framework, namely Happy, characterized by Hardness-aware prototype sampling and soft entropy regularization.
  arXiv  Detail & Related papers  (2024-10-09T04:18:51Z)
• Cross Branch Feature Fusion Decoder for Consistency Regularization-based Semi-Supervised Change Detection [9.147851499947228]
  We introduce a new decoder called Cross Branch Feature Fusion CBFF. It combines the strengths of both local convolutional branch and global transformer branch. Using CBFF, we build our SSCD model based on a strong-to-weak consistency strategy.
  arXiv  Detail & Related papers  (2024-09-23T13:47:59Z)
• SeFi-CD: A Semantic First Change Detection Paradigm That Can Detect Any Change You Want [3.8287068443608567]
  This paper introduces a new CD paradigm, the semantic-first CD (SeFi-CD) paradigm. The core idea of SeFi-CD is to first perceive the dynamic semantics of interest and then visually search for change features related to the semantics. Experiments on public datasets demonstrate that the AUWCD outperforms the current state-of-the-art CD methods.
  arXiv  Detail & Related papers  (2024-07-13T12:49:58Z)
• ChangeAnywhere: Sample Generation for Remote Sensing Change Detection via Semantic Latent Diffusion Model [4.677012401985776]
  ChangeAnywhere is a novel CD sample generation method using the semantic latent diffusion model and single-temporal images. ChangeAnywhere captures the two essentials of CD samples, i.e. change implies semantically different, and non-change implies reasonable change under the same semantic constraints. The ChangeAnywhere-100K significantly improved both zero-shot and few-shot performance on two CD benchmark datasets for various deep learning-based CD models.
  arXiv  Detail & Related papers  (2024-04-13T03:46:35Z)
• Active Generalized Category Discovery [60.69060965936214]
  Generalized Category Discovery (GCD) endeavors to cluster unlabeled samples from both novel and old classes. We take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD) Our method achieves state-of-the-art performance on both generic and fine-grained datasets.
  arXiv  Detail & Related papers  (2024-03-07T07:12:24Z)
• Exploring Effective Priors and Efficient Models for Weakly-Supervised Change Detection [9.229278131265124]
  Weakly-supervised change detection (WSCD) aims to detect pixel-level changes with only image-level annotations. We propose two components: a Dilated Prior (DP) decoder and a Label Gated (LG) constraint. Our proposed TransWCD and TransWCD-DL achieve significant +6.33% and +9.55% F1 score improvements over the state-of-the-art methods on the WHU-CD dataset.
  arXiv  Detail & Related papers  (2023-07-20T13:16:10Z)
• GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPM [7.922421805234563]
  Deep learning methods have recently shown great promise in bitemporal change detection (CD) This work proposes a generative change detection model called GCD-DDPM to directly generate CD maps. Experiments on four high-resolution CD datasets confirm the superior performance of the proposed GCD-DDPM.
  arXiv  Detail & Related papers  (2023-06-06T05:51:50Z)
• Dynamic Conceptional Contrastive Learning for Generalized Category Discovery [76.82327473338734]
  Generalized category discovery (GCD) aims to automatically cluster partially labeled data. Unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. We propose a Dynamic Conceptional Contrastive Learning framework, which can effectively improve clustering accuracy.
  arXiv  Detail & Related papers  (2023-03-30T14:04:39Z)
• Deep Metric Learning for Unsupervised Remote Sensing Change Detection [60.89777029184023]
  Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs) The performance of existing RS-CD methods is attributed to training on large annotated datasets. This paper proposes an unsupervised CD method based on deep metric learning that can deal with both of these issues.
  arXiv  Detail & Related papers  (2023-03-16T17:52:45Z)
• Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images [60.89777029184023]
  We propose a semi-supervised CD model in which we formulate an unsupervised CD loss in addition to the supervised Cross-Entropy (CE) loss. Experiments conducted on two publicly available CD datasets show that the proposed semi-supervised CD method can reach closer to the performance of supervised CD.
  arXiv  Detail & Related papers  (2022-04-18T17:59:01Z)

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.