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Related Experiment Video

Updated: Nov 25, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation.

Christos Sakaridis, Dengxin Dai, Luc Van Gool

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 18, 2020
    PubMed
    Summary
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    This study adapts daytime semantic segmentation models for nighttime use without nighttime data. A novel evaluation framework handles nighttime image uncertainty, improving performance and safety applications.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Semantic nighttime image segmentation is challenging due to poor visibility and lack of labeled data.
    • Existing daytime models perform poorly on nighttime images without adaptation.
    • Substantial uncertainty exists in nighttime image semantics, complicating evaluation.

    Purpose of the Study:

    • To adapt daytime semantic segmentation models for nighttime conditions without using nighttime annotations.
    • To develop a novel evaluation framework and metric that accounts for uncertainty in nighttime image semantics.
    • To introduce the Dark Zurich dataset as a benchmark for nighttime semantic segmentation.

    Main Methods:

    • A curriculum framework progressively adapts models from day to night using cross-time-of-day correspondences.

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  • Exploiting reference maps and progressively darker times of day to guide label inference.
  • Developing an uncertainty-aware annotation and evaluation framework, including regions beyond human recognition.

    • Main Results:

    • Map-guided curriculum adaptation significantly outperforms state-of-the-art methods on nighttime datasets.
    • The novel uncertainty-aware metric provides a more principled evaluation for nighttime images.
    • Selective invalidation of predictions improves results on ambiguous data and benefits safety applications.

    Conclusions:

    • Daytime semantic segmentation models can be effectively adapted for nighttime use via a curriculum learning approach.
    • The proposed uncertainty-aware evaluation framework is crucial for reliable nighttime image analysis.
    • The Dark Zurich dataset and new evaluation metric establish a benchmark for future research in this domain.