# Weakly Supervised Silhouette-based Semantic Scene Change Detection

arXiv: https://arxiv.org/abs/1811.11985 · HTML: https://mlsanity.com/paper/1811.11985/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 30 of 44)

- [Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks](https://mlsanity.com/paper/2112.01715/) (2021)
- [DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection](https://mlsanity.com/paper/2103.00879/) (2021)
- [ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments](https://mlsanity.com/paper/2103.05368/) (2021)
- [How to Reduce Change Detection to Semantic Segmentation](https://mlsanity.com/paper/2206.07557/) (2022)
- [The Change You Want to See (Now in 3D)](https://mlsanity.com/paper/2308.10417/) (2023)
- [Towards Generalizable Scene Change Detection](https://mlsanity.com/paper/2409.06214/) (2024)
- [Zero-Shot Scene Change Detection](https://mlsanity.com/paper/2406.11210/) (2024)
- [ZeroSCD: Zero-Shot Street Scene Change Detection](https://mlsanity.com/paper/2409.15255/) (2024)
- [Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms](https://mlsanity.com/paper/2409.16850/) (2024)
- [Multi-View Pose-Agnostic Change Localization with Zero Labels](https://mlsanity.com/paper/2412.03911/) (2024)
- [Background-Mixed Augmentation for Weakly Supervised Change Detection](https://mlsanity.com/paper/2211.11478/) (2022)
- [CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series](https://mlsanity.com/paper/2401.01107/) (2024)
- [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)
- [UMAD: University of Macau Anomaly Detection Benchmark Dataset](https://mlsanity.com/paper/2408.12527/) (2024)
- [ViewDelta: Scaling Scene Change Detection through Text-Conditioning](https://mlsanity.com/paper/2412.07612/) (2024)
- [SceneDiff: A Benchmark and Method for Multiview Object Change Detection](https://mlsanity.com/paper/2512.16908/) (2025)
- [Domain Invariant Siamese Attention Mask for Small Object Change Detection via Everyday Indoor Robot Navigation](https://mlsanity.com/paper/2203.15362/) (2022)
- [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)
- [Advancing Weakly-Supervised Change Detection in Satellite Images via Adversarial Class Prompting](https://mlsanity.com/paper/2508.17186/) (2025)
- [STATUS Bench: A Rigorous Benchmark for Evaluating Object State Understanding in Vision-Language Models](https://mlsanity.com/paper/2510.22571/) (2025)
- [Changes in Real Time: Online Scene Change Detection with Multi-View Fusion](https://mlsanity.com/paper/2511.12370/) (2025)
- [3D Scene Change Modeling With Consistent Multi-View Aggregation](https://mlsanity.com/paper/2512.22830/) (2025)
- [The Change You Want to See](https://mlsanity.com/paper/2209.14341/) (2022)
- [Lifelong Change Detection: Continuous Domain Adaptation for Small Object Change Detection in Every Robot Navigation](https://mlsanity.com/paper/2306.16086/) (2023)
- [Information-Bottleneck Driven Binary Neural Network for Change Detection](https://mlsanity.com/paper/2507.03504/) (2025)
- [Differencing based Self-supervised pretraining for Scene Change Detection](https://mlsanity.com/paper/2208.05838/) (2022)
- [SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images](https://mlsanity.com/paper/2303.04737/) (2023)
- [Environmental Change Detection for Real-World Change Analysis](https://mlsanity.com/paper/2506.11481/) (2025)

## Abstract (arXiv)

This paper presents a novel semantic scene change detection scheme with only weak supervision. A straightforward approach for this task is to train a semantic change detection network directly from a large-scale dataset in an end-to-end manner. However, a specific dataset for this task, which is usually labor-intensive and time-consuming, becomes indispensable. To avoid this problem, we propose to train this kind of network from existing datasets by dividing this task into change detection and semantic extraction. On the other hand, the difference in camera viewpoints, for example, images of the same scene captured from a vehicle-mounted camera at different time points, usually brings a challenge to the change detection task. To address this challenge, we propose a new siamese network structure with the introduction of correlation layer. In addition, we collect and annotate a publicly available dataset for semantic change detection to evaluate the proposed method. The experimental results verified both the robustness to viewpoint difference in change detection task and the effectiveness for semantic change detection of the proposed networks. Our code and dataset are available at https://kensakurada.github.io/pscd.