Related papers: A Change Detection Reality Check

Related papers

Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practices [0.0]
Remote sensing change detection aims to localize semantic changes between images of the same location captured at different times.<n>Most fail to measure the performance contribution of fundamental design choices such as backbone selection, pre-training strategies, and training configurations.<n>We claim that such fundamental design choices often improve performance even more significantly than the addition of new architectural components.
arXiv Detail & Related papers (2025-07-04T08:01:28Z)
A Comparative Study of U-Net Architectures for Change Detection in Satellite Images [0.0]
The U-Net architecture has gained popularity for its capability to capture spatial information and perform pixel-wise classification.<n>This study conducts a comparison and analysis of 18 different U-Net variations, assessing their potential for detecting changes in remote sensing.
arXiv Detail & Related papers (2025-06-09T16:38:34Z)
OpenTAD: A Unified Framework and Comprehensive Study of Temporal Action Detection [86.30994231610651]
Temporal action detection (TAD) is a fundamental video understanding task that aims to identify human actions and localize their temporal boundaries in videos. We propose textbfOpenTAD, a unified TAD framework consolidating 16 different TAD methods and 9 standard datasets into a modular framework. Minimal effort is required to replace one module with a different design, train a feature-based TAD model in end-to-end mode, or switch between the two.
arXiv Detail & Related papers (2025-02-27T18:32:27Z)
Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detection [82.65760006883248]
We introduce a new task named Change Detection Question Answering and Grounding (CDQAG) CDQAG extends the traditional change detection task by providing interpretable textual answers and intuitive visual evidence. We construct the first CDQAG benchmark dataset, termed QAG-360K, comprising over 360K triplets of questions, textual answers, and corresponding high-quality visual masks.
arXiv Detail & Related papers (2024-10-31T11:20:13Z)
Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey [2.9373912230684565]
Change detection aims to analyze changes in surface areas over time and has broad applications in areas such as environmental monitoring, urban development, and land use analysis. Deep learning, especially the development of foundation models, has provided more powerful solutions for feature extraction and data fusion. This paper systematically reviews the latest advancements in the field of change detection, with a focus on the application of foundation models in remote sensing tasks.
arXiv Detail & Related papers (2024-10-10T11:16:05Z)
Novel Change Detection Framework in Remote Sensing Imagery Using Diffusion Models and Structural Similarity Index (SSIM) [0.0]
Change detection is a crucial task in remote sensing, enabling the monitoring of environmental changes, urban growth, and disaster impact. Recent advancements in machine learning, particularly generative models like diffusion models, offer new opportunities for enhancing change detection accuracy. We propose a novel change detection framework that combines the strengths of Stable Diffusion models with the Structural Similarity Index (SSIM) to create robust and interpretable change maps.
arXiv Detail & Related papers (2024-08-20T07:54:08Z)
A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
arXiv Detail & Related papers (2024-06-05T13:40:07Z)
Zero-shot Degree of Ill-posedness Estimation for Active Small Object Change Detection [8.977792536037956]
In everyday indoor navigation, robots often needto detect non-distinctive small-change objects. Existing techniques rely on high-quality class-specific object priors to regularize a change detector model. In this study, we explore the concept of degree-of-ill-posedness (DoI) to improve both passive and activevision.
arXiv Detail & Related papers (2024-05-10T01:56:39Z)
Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review [45.78958623050146]
Change detection is an essential and widely utilized task in remote sensing. It aims to detect and analyze changes occurring in the same geographical area over time. Deep learning has emerged as a powerful tool for feature extraction and addressing these challenges.
arXiv Detail & Related papers (2023-05-09T23:52:37Z)
Demystify Transformers & Convolutions in Modern Image Deep Networks [82.32018252867277]
This paper aims to identify the real gains of popular convolution and attention operators through a detailed study. We find that the key difference among these feature transformation modules, such as attention or convolution, lies in their spatial feature aggregation approach. Our experiments on various tasks and an analysis of inductive bias show a significant performance boost due to advanced network-level and block-level designs.
arXiv Detail & Related papers (2022-11-10T18:59:43Z)
Revisiting Consistency Regularization for Semi-Supervised Learning [80.28461584135967]
We propose an improved consistency regularization framework by a simple yet effective technique, FeatDistLoss. Experimental results show that our model defines a new state of the art for various datasets and settings.
arXiv Detail & Related papers (2021-12-10T20:46:13Z)
Deep few-shot learning for bi-temporal building change detection [0.0]
A new deep few-shot learning method is proposed for building change detection using Monte Carlo dropout and remote sensing observations. The setup is based on a small dataset, including bitemporal optical images labeled for building change detection.
arXiv Detail & Related papers (2021-08-25T14:38:21Z)
Deep learning approaches to Earth Observation change detection [0.0]
We present two approaches to change detection (semantic segmentation and classification) that both exploit convolutional neural networks to achieve good results. In this paper we present two different approaches to change detection (semantic segmentation and classification) that both exploit convolutional neural networks to achieve good results.
arXiv Detail & Related papers (2021-07-13T14:34:59Z)
Stance Detection Benchmark: How Robust Is Your Stance Detection? [65.91772010586605]
Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim. We introduce a StD benchmark that learns from ten StD datasets of various domains in a multi-dataset learning setting. Within this benchmark setup, we are able to present new state-of-the-art results on five of the datasets.
arXiv Detail & Related papers (2020-01-06T13:37:51Z)

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