# Curriculum-guided Change Detection Training: Toward Accurate Serac Fall Monitoring

arXiv: https://arxiv.org/abs/2606.28012 · HTML: https://mlsanity.com/paper/2606.28012/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 26 Jun 2026.

## Builds on (most cited first, 29 of 29)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling](https://mlsanity.com/paper/2110.08263/) (2021)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning](https://mlsanity.com/paper/2205.07246/) (2022)
- [Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2208.09910/) (2022)
- [Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks](https://mlsanity.com/paper/1802.03796/) (2018)
- [STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation](https://mlsanity.com/paper/1509.03150/) (2015)
- [UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2410.10777/) (2024)
- [How hard can it be? Estimating the difficulty of visual search in an image](https://mlsanity.com/paper/1705.08280/) (2017)
- [DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection](https://mlsanity.com/paper/2103.00879/) (2021)
- [Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2204.08454/) (2022)
- [How to Reduce Change Detection to Semantic Segmentation](https://mlsanity.com/paper/2206.07557/) (2022)
- [Denoising Task Difficulty-based Curriculum for Training Diffusion Models](https://mlsanity.com/paper/2403.10348/) (2024)
- [Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)](https://mlsanity.com/paper/1910.08967/) (2019)
- [SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-supervised Change Detector](https://mlsanity.com/paper/2405.04788/) (2024)
- [Latent Intrinsics Emerge from Training to Relight](https://mlsanity.com/paper/2405.21074/) (2024)
- [ZeroSCD: Zero-Shot Street Scene Change Detection](https://mlsanity.com/paper/2409.15255/) (2024)
- [From Prototypes to General Distributions: An Efficient Curriculum for Masked Image Modeling](https://mlsanity.com/paper/2411.10685/) (2024)
- [Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning](https://mlsanity.com/paper/2405.18376/) (2024)
- [Improving Zero-Shot Object-Level Change Detection by Incorporating Visual Correspondence](https://mlsanity.com/paper/2501.05555/) (2025)
- [Revisiting Change Detection Methods for their Application to Serac Fall Time-Lapse Monitoring](https://mlsanity.com/paper/2605.28100/) (2026)

## Abstract (arXiv)

Change Detection (CD) aims to identify semantic or structural changes from nearly registered multi-temporal images. While recent advances in training methodologies have largely focused on semi-supervised learning and consistency regularization, alternative training paradigms remain underexplored. In particular, most deep CD methods rely on uniform sampling during training, implicitly assuming that all training samples contribute equally to the optimization process. However, such naive sampling can introduce noisy gradients and hinder robust representation learning. To address this limitation, we propose a curriculum learning framework tailored for change detection. Our approach investigates two complementary difficulty measures: the Solar Angular Gap (SAG), a physically grounded proxy for acquisition-condition variability, and the Structural Similarity Index Measure (SSIM), which evaluates appearance similarity between image pairs. Based on these criteria, the framework progressively introduces challenging samples during training, enabling models to learn robust representations in a coarse-to-fine manner. We evaluate our method on the challenging SeracFallDet benchmark, where results demonstrate consistent improvements of the proposed approach over standard uniform-sampling strategies for both pixel-based and object-based approaches. These results highlight the potential of curriculum learning to improve robustness in deep change detection. Importantly, our training framework is orthogonal to existing CD architectures, making it readily applicable to a broad range of methods.