# Cooperative Students: Navigating Unsupervised Domain Adaptation in Nighttime Object Detection

Source: [Cooperative Students: Navigating Unsupervised Domain Adaptation in Nighttime Object Detection](<https://www.sota2.com/research/paper/68fa79d71e209131cea7faae>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:06:33.533Z

Authors: Jicheng Yuan, Anh Le-Tuan, Manfred Hauswirth, Danh Le-Phuoc

Published: 2024-04-02T14:26:18.000Z

## Abstract

Unsupervised Domain Adaptation (UDA) has shown significant advancements in object detection under well-lit conditions; however, its performance degrades notably in low-visibility scenarios, especially at night, posing challenges not only for its adaptability in low signal-to-noise ratio (SNR) conditions but also for the reliability and efficiency of automated vehicles. To address this problem, we propose a \\textbf{Co}operative \\textbf{S}tudents (\\textbf{CoS}) framework that innovatively employs global-local transformations (GLT) and a proxy-based target consistency (PTC) mechanism to capture the spatial consistency in day- and night-time scenarios effectively, and thus bridge the significant domain shift across contexts. Building upon this, we further devise an adaptive IoU-informed thresholding (AIT) module to gradually avoid overlooking potential true positives and enrich the latent information in the target domain. Comprehensive experiments show that CoS essentially enhanced UDA performance in low-visibility conditions and surpasses current state-of-the-art techniques, achieving an increase in mAP of 3.0\\%, 1.9\\%, and 2.5\\% on BDD100K, SHIFT, and ACDC datasets, respectively. Code is available at https://github.com/jichengyuan/Cooperitive\_Students.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.01988>)

[PDF](<https://arxiv.org/pdf/2404.01988.pdf>)