# 2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection

arXiv: https://arxiv.org/abs/2303.13853 · HTML: https://mlsanity.com/paper/2303.13853/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 41 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020)
- [Domain Adaptive Faster R-CNN for Object Detection in the Wild](https://mlsanity.com/paper/1803.03243/) (2018)
- [Contrastive Adaptation Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1901.00976/) (2019)
- [Unsupervised Domain Adaptation with Residual Transfer Networks](https://mlsanity.com/paper/1602.04433/) (2016)
- [Strong-Weak Distribution Alignment for Adaptive Object Detection](https://mlsanity.com/paper/1812.04798/) (2018)
- [Unbiased Mean Teacher for Cross-domain Object Detection](https://mlsanity.com/paper/2003.00707/) (2020)
- [SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation](https://mlsanity.com/paper/2206.08367/) (2022)
- [Exploring Object Relation in Mean Teacher for Cross-Domain Detection](https://mlsanity.com/paper/1904.11245/) (2019)
- [Exploring Categorical Regularization for Domain Adaptive Object Detection](https://mlsanity.com/paper/2003.09152/) (2020)
- [Cross-Domain Adaptive Teacher for Object Detection](https://mlsanity.com/paper/2111.13216/) (2021)
- [Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime](https://mlsanity.com/paper/1810.02575/) (2018)
- [Cross-domain Detection via Graph-induced Prototype Alignment](https://mlsanity.com/paper/2003.12849/) (2020)
- [DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation](https://mlsanity.com/paper/2104.10834/) (2021)
- [Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation](https://mlsanity.com/paper/2204.03838/) (2022)
- [Unsupervised Domain Adaptation for Nighttime Aerial Tracking](https://mlsanity.com/paper/2203.10541/) (2022)
- [Zero-Shot Day-Night Domain Adaptation with a Physics Prior](https://mlsanity.com/paper/2108.05137/) (2021)
- [Cross Domain Object Detection by Target-Perceived Dual Branch Distillation](https://mlsanity.com/paper/2205.01291/) (2022)
- [Cross-Domain Correlation Distillation for Unsupervised Domain Adaptation in Nighttime Semantic Segmentation](https://mlsanity.com/paper/2205.00858/) (2022)
- [NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night](https://mlsanity.com/paper/2204.05538/) (2022)
- [NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset](https://mlsanity.com/paper/2110.10364/) (2021)

## Built on by (most cited first, 30 of 31)

- [ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones](https://mlsanity.com/paper/2406.07661/) (2024)
- [CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection](https://mlsanity.com/paper/2403.19278/) (2024)
- [Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation](https://mlsanity.com/paper/2312.01220/) (2023)
- [Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2403.05810/) (2024)
- [Large Self-Supervised Models Bridge the Gap in Domain Adaptive Object Detection](https://mlsanity.com/paper/2503.23220/) (2025)
- [JoReS-Diff: Joint Retinex and Semantic Priors in Diffusion Model for Low-light Image Enhancement](https://mlsanity.com/paper/2312.12826/) (2023)
- [Instance-Warp: Saliency Guided Image Warping for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2403.12712/) (2024)
- [Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in Extremely Low-Light Conditions](https://mlsanity.com/paper/2407.15451/) (2024)
- [Semantic Segmentation in Multiple Adverse Weather Conditions with Domain Knowledge Retention](https://mlsanity.com/paper/2401.07459/) (2024)
- [DODA: Adapting Object Detectors to Dynamic Agricultural Environments in Real-Time with Diffusion](https://mlsanity.com/paper/2403.18334/) (2024)
- [Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation](https://mlsanity.com/paper/2404.03799/) (2024)
- [Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View](https://mlsanity.com/paper/2407.12870/) (2024)
- [Few-Shot Learning from Augmented Label-Uncertain Queries in Bongard-HOI](https://mlsanity.com/paper/2312.10586/) (2023)
- [Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets](https://mlsanity.com/paper/2506.04737/) (2025)
- [Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments](https://mlsanity.com/paper/2404.17930/) (2024)
- [Spatially Visual Perception for End-to-End Robotic Learning](https://mlsanity.com/paper/2411.17458/) (2024)
- [Adversarial Defense Teacher for Cross-Domain Object Detection under Poor Visibility Conditions](https://mlsanity.com/paper/2403.15786/) (2024)
- [Mitigating the Impact of Prominent Position Shift in Drone-based RGBT Object Detection](https://mlsanity.com/paper/2502.09311/) (2025)
- [Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions](https://mlsanity.com/paper/2312.06169/) (2023)
- [Cooperative Students: Navigating Unsupervised Domain Adaptation in Nighttime Object Detection](https://mlsanity.com/paper/2404.01988/) (2024)
- [Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment](https://mlsanity.com/paper/2411.16772/) (2024)
- [WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver Assistance Systems](https://mlsanity.com/paper/2505.23201/) (2025)
- [The Devil is in the Darkness: Diffusion-Based Nighttime Dehazing Anchored in Brightness Perception](https://mlsanity.com/paper/2506.02395/) (2025)
- [UniDet-D: A Unified Dynamic Spectral Attention Model for Object Detection under Adverse Weathers](https://mlsanity.com/paper/2506.12324/) (2025)
- [TTA-DAME: Test-Time Adaptation with Domain Augmentation and Model Ensemble for Dynamic Driving Conditions](https://mlsanity.com/paper/2508.12690/) (2025)
- [Domain Adaptive Object Detection for Space Applications with Real-Time Constraints](https://mlsanity.com/paper/2509.17593/) (2025)
- [PAGen: Phase-guided Amplitude Generation for Domain-adaptive Object Detection](https://mlsanity.com/paper/2511.22029/) (2025)
- [AMIEOD: Adaptive Multi-Experts Image Enhancement for Object Detection in Low-Illumination Scenes](https://mlsanity.com/paper/2605.06084/) (2026)
- [MUSDA: Multi-source Multi-modality Unsupervised Domain Adaptive 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2605.10026/) (2026)
- [Ultra: Unsupervised Cross-Task Optimization for Reliable Restoration Segmentation Collaboration under Adverse Weather](https://mlsanity.com/paper/2608.16589/) (2026)

## Abstract (arXiv)

Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remains an issue. False-positive error propagation is still observed in methods using the well-established student-teacher framework, particularly for small-scale and low-light objects. This paper proposes a two-phase consistency unsupervised domain adaptation network, 2PCNet, to address these issues. The network employs high-confidence bounding-box predictions from the teacher in the first phase and appends them to the student's region proposals for the teacher to re-evaluate in the second phase, resulting in a combination of high and low confidence pseudo-labels. The night images and pseudo-labels are scaled-down before being used as input to the student, providing stronger small-scale pseudo-labels. To address errors that arise from low-light regions and other night-related attributes in images, we propose a night-specific augmentation pipeline called NightAug. This pipeline involves applying random augmentations, such as glare, blur, and noise, to daytime images. Experiments on publicly available datasets demonstrate that our method achieves superior results to state-of-the-art methods by 20%, and to supervised models trained directly on the target data.