# Lifelong Change Detection: Continuous Domain Adaptation for Small Object Change Detection in Every Robot Navigation

arXiv: https://arxiv.org/abs/2306.16086 · HTML: https://mlsanity.com/paper/2306.16086/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 28 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [Confidence Regularized Self-Training](https://mlsanity.com/paper/1908.09822/) (2019)
- [Asymmetric Tri-training for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1702.08400/) (2017)
- [Harmonizing Transferability and Discriminability for Adapting Object Detectors](https://mlsanity.com/paper/2003.06297/) (2020)
- [Learning Accurate Dense Correspondences and When to Trust Them](https://mlsanity.com/paper/2101.01710/) (2021)
- [Self-Training: A Survey](https://mlsanity.com/paper/2202.12040/) (2022)
- [Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification](https://mlsanity.com/paper/1702.05891/) (2017)
- [Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning](https://mlsanity.com/paper/2012.04324/) (2020)
- [Incremental Adversarial Domain Adaptation for Continually Changing Environments](https://mlsanity.com/paper/1712.07436/) (2017)
- [Weakly Supervised Silhouette-based Semantic Scene Change Detection](https://mlsanity.com/paper/1811.11985/) (2018)
- [DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection](https://mlsanity.com/paper/2103.00879/) (2021)
- [Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints](https://mlsanity.com/paper/1712.02941/) (2017)

## Built on by (most cited first, 2 of 2)

- [C-NERF: Representing Scene Changes as Directional Consistency Difference-based NeRF](https://mlsanity.com/paper/2312.02751/) (2023)
- [Zero-shot Degree of Ill-posedness Estimation for Active Small Object Change Detection](https://mlsanity.com/paper/2405.06185/) (2024)

## Abstract (arXiv)

The recently emerging research area in robotics, ground view change detection, suffers from its ill-posed-ness because of visual uncertainty combined with complex nonlinear perspective projection. To regularize the ill-posed-ness, the commonly applied supervised learning methods (e.g., CSCD-Net) rely on manually annotated high-quality object-class-specific priors. In this work, we consider general application domains where no manual annotation is available and present a fully self-supervised approach. The present approach adopts the powerful and versatile idea that object changes detected during everyday robot navigation can be reused as additional priors to improve future change detection tasks. Furthermore, a robustified framework is implemented and verified experimentally in a new challenging practical application scenario: ground-view small object change detection.