mlsanityData to 7 Oct 2026

Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting

arXiv 2302.04832 ↗First posted 9 Feb 2023cs.CVcited by 9

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Abstract (arXiv)

Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting from a labeled synthetic source domain to an unlabeled or sparsely labeled real target domain. However, for high-stakes applications (e.g. autonomous driving), it is common to have a modest amount of human-labeled real data in addition to plentiful auto-labeled source data (e.g. from a driving simulator). We study this setting of supervised sim2real DA applied to 2D object detection. We propose Domain Translation via Conditional Alignment and Reweighting (CARE) a novel algorithm that systematically exploits target labels to explicitly close the sim2real appearance and content gaps. We present an analytical justification of our algorithm and demonstrate strong gains over competing methods on standard benchmarks.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 43 of its 55 references that are in the map. First posted on arXiv 9 Feb 2023.

Builds on

43 of 55 references.

References · 43

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31 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 3; 2026: 1.

9 papers.

  1. 2023Synthetic Datasets for Autonomous Driving: A Surveycited by 48
  2. 2023Text-to-Image Models for Counterfactual Explanations: a Black-Box Approachcited by 28
  3. 2024Global Counterfactual Directionscited by 15
  4. 2023A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Drivingcited by 7
  5. 2025Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AIcited by 2
  6. 2024OSSA: Unsupervised One-Shot Style Adaptationcited by 0
  7. 2025synth-dacl: Does Synthetic Defect Data Enhance Segmentation Accuracy and Robustness for Real-World Bridge Inspections?cited by 0
  8. 2025Synthetic Data for Robust Runway Detectioncited by 0
  9. 2026From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Drivingcited by 0