# ZeroSCD: Zero-Shot Street Scene Change Detection

arXiv: https://arxiv.org/abs/2409.15255 · HTML: https://mlsanity.com/paper/2409.15255/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images](https://mlsanity.com/paper/2003.03608/) (2020)
- [Weakly Supervised Silhouette-based Semantic Scene Change Detection](https://mlsanity.com/paper/1811.11985/) (2018)
- [Hierarchical Paired Channel Fusion Network for Street Scene Change Detection](https://mlsanity.com/paper/2010.09925/) (2020)
- [Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection](https://mlsanity.com/paper/1810.09111/) (2018)
- [Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2212.10236/) (2022)
- [Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints](https://mlsanity.com/paper/1712.02941/) (2017)
- [Zero-Shot Scene Change Detection](https://mlsanity.com/paper/2406.11210/) (2024)
- [PlaceFormer: Transformer-based Visual Place Recognition using Multi-Scale Patch Selection and Fusion](https://mlsanity.com/paper/2401.13082/) (2024)

## Built on by (most cited first, 12 of 12)

- [Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis](https://mlsanity.com/paper/2603.22786/) (2026)
- [Changes in Real Time: Online Scene Change Detection with Multi-View Fusion](https://mlsanity.com/paper/2511.12370/) (2025)
- [From Pixels to Primitives: Scene Change Detection in 3D Gaussian Splatting](https://mlsanity.com/paper/2605.07203/) (2026)
- [Environmental Change Detection for Real-World Change Analysis](https://mlsanity.com/paper/2506.11481/) (2025)
- [Revisiting Change Detection Methods for their Application to Serac Fall Time-Lapse Monitoring](https://mlsanity.com/paper/2605.28100/) (2026)
- [PairWise Image Finder: An Open-source Tool for Finding Visually Aligned Street-Level Image Pairs for Urban Perception Studies](https://mlsanity.com/paper/2606.08795/) (2026)
- [ReA-OVCD: Training-Free Open-Vocabulary Change Detection via Semantic-Spatial Reliability Assessment](https://mlsanity.com/paper/2606.20032/) (2026)
- [PlenoCI: Plenoptic CharacterIstics for View Dependence Aware Change Classification](https://mlsanity.com/paper/2609.28930/) (2026)
- [Generalization-aware Remote Sensing Change Detection via Domain-agnostic Learning](https://mlsanity.com/paper/2504.00543/) (2025)
- [TERDNet: Transformer Encoder-Recurrent Decoder Network for Scene Change Detection](https://mlsanity.com/paper/2605.20822/) (2026)
- [Curriculum-guided Change Detection Training: Toward Accurate Serac Fall Monitoring](https://mlsanity.com/paper/2606.28012/) (2026)
- [Spot-the-shift: Evaluating Grounded Image Difference Captioning of Long-term Changes](https://mlsanity.com/paper/2609.10356/) (2026)

## Abstract (arXiv)

Scene Change Detection is a challenging task in computer vision and robotics that aims to identify differences between two images of the same scene captured at different times. Traditional change detection methods rely on training models that take these image pairs as input and estimate the changes, which requires large amounts of annotated data, a costly and time-consuming process. To overcome this, we propose ZeroSCD, a zero-shot scene change detection framework that eliminates the need for training. ZeroSCD leverages pre-existing models for place recognition and semantic segmentation, utilizing their features and outputs to perform change detection. In this framework, features extracted from the place recognition model are used to estimate correspondences and detect changes between the two images. These are then combined with segmentation results from the semantic segmentation model to precisely delineate the boundaries of the detected changes. Extensive experiments on benchmark datasets demonstrate that ZeroSCD outperforms several state-of-the-art methods in change detection accuracy, despite not being trained on any of the benchmark datasets, proving its effectiveness and adaptability across different scenarios.