# Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection

arXiv: https://arxiv.org/abs/1810.09111 · HTML: https://mlsanity.com/paper/1810.09111/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2018.

## Built on by (most cited first, 25 of 25)

- [DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection](https://mlsanity.com/paper/2103.00879/) (2021)
- [How to Reduce Change Detection to Semantic Segmentation](https://mlsanity.com/paper/2206.07557/) (2022)
- [Remote Sensing Change Detection With Transformers Trained from Scratch](https://mlsanity.com/paper/2304.06710/) (2023)
- [ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection](https://mlsanity.com/paper/2403.17909/) (2024)
- [Fully Transformer Network for Change Detection of Remote Sensing Images](https://mlsanity.com/paper/2210.00757/) (2022)
- [TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images](https://mlsanity.com/paper/2310.14214/) (2023)
- [ZeroSCD: Zero-Shot Street Scene Change Detection](https://mlsanity.com/paper/2409.15255/) (2024)
- [An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs](https://mlsanity.com/paper/2105.01342/) (2021)
- [ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection](https://mlsanity.com/paper/2303.14679/) (2023)
- [Weakly Supervised Change Detection Using Guided Anisotropic Difusion](https://mlsanity.com/paper/2112.15367/) (2021)
- [City-scale Scene Change Detection using Point Clouds](https://mlsanity.com/paper/2103.14314/) (2021)
- [Diff-Net: Image Feature Difference based High-Definition Map Change Detection for Autonomous Driving](https://mlsanity.com/paper/2107.07030/) (2021)
- [IDET: Iterative Difference-Enhanced Transformers for High-Quality Change Detection](https://mlsanity.com/paper/2207.09240/) (2022)
- [Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robots](https://mlsanity.com/paper/2209.02379/) (2022)
- [Contextual Scene Augmentation and Synthesis via GSACNet](https://mlsanity.com/paper/2103.15369/) (2021)
- [Automatic Map Update Using Dashcam Videos](https://mlsanity.com/paper/2109.12131/) (2021)
- [Differencing based Self-supervised pretraining for Scene Change Detection](https://mlsanity.com/paper/2208.05838/) (2022)
- [Industrial Scene Change Detection using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2212.14278/) (2022)
- [SeFi-CD: A Semantic First Change Detection Paradigm That Can Detect Any Change You Want](https://mlsanity.com/paper/2407.09874/) (2024)
- [Crowd Source Scene Change Detection and Local Map Update](https://mlsanity.com/paper/2203.05205/) (2022)
- [Semi-Siamese Network for Robust Change Detection Across Different Domains with Applications to 3D Printing](https://mlsanity.com/paper/2212.08583/) (2022)
- [MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation](https://mlsanity.com/paper/2402.00918/) (2024)
- [HSONet:A Siamese foreground association-driven hard case sample optimization network for high-resolution remote sensing image change detection](https://mlsanity.com/paper/2402.16242/) (2024)
- [Change-Aware Siamese Network for Surface Defects Segmentation under Complex Background](https://mlsanity.com/paper/2409.00589/) (2024)
- [Generalization-aware Remote Sensing Change Detection via Domain-agnostic Learning](https://mlsanity.com/paper/2504.00543/) (2025)

## Abstract (arXiv)

A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled. Following the intuitive idea of detecting changes by directly comparing dissimilarities between a pair of features, we propose a novel fully Convolutional siamese metric Network(CosimNet) to measure changes by customizing implicit metrics. To learn more discriminative metrics, we utilize contrastive loss to reduce the distance between the unchanged feature pairs and to enlarge the distance between the changed feature pairs. Specifically, to address the issue of large viewpoint differences, we propose Thresholded Contrastive Loss (TCL) with a more tolerant strategy to punish noisy changes. We demonstrate the effectiveness of the proposed approach with experiments on three challenging datasets: CDnet, PCD2015, and VL-CMU-CD. Our approach is robust to lots of challenging conditions, such as illumination changes, large viewpoint difference caused by camera motion and zooming. In addition, we incorporate the distance metric into the segmentation framework and validate the effectiveness through visualization of change maps and feature distribution. The source code is available at https://github.com/gmayday1997/ChangeDet.