mlsanityData to 14 Oct 2026

The BRAVO Semantic Segmentation Challenge Results in UNCV2024

arXiv 2409.15107 ↗First posted 23 Sep 2024cs.CV cs.AI cs.LGcited by 6

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Abstract (arXiv)

We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios. We define two categories of reliability: (1) semantic reliability, which reflects the model's accuracy and calibration when exposed to various perturbations; and (2) OOD reliability, which measures the model's ability to detect object classes that are unknown during training. The challenge attracted nearly 100 submissions from international teams representing notable research institutions. The results reveal interesting insights into the importance of large-scale pre-training and minimal architectural design in developing robust and reliable semantic segmentation models.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 26 of its 36 references that are in the map. First posted on arXiv 23 Sep 2024.

Builds on

26 of 36 references.

References · 26

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  12. 2020Hierarchical Multi-Scale Attention for Semantic Segmentationcited by 183
14 more
  1. 2018IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environmentscited by 163
  2. 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
  3. 2022SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptationcited by 146
  4. 2022RbA: Segmenting Unknown Regions Rejected by Allcited by 67
  5. 2020How to Train Neural Networks for Flare Removalcited by 50
  6. 2021Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentationcited by 40
  7. 2023A Simple Recipe for Language-guided Domain Generalized Segmentationcited by 28
  8. 2023Reliability in Semantic Segmentation: Can We Use Synthetic Data?cited by 24
  9. 2024PixOOD: Pixel-Level Out-of-Distribution Detectioncited by 20
  10. 2021Robust Semantic Segmentation with Superpixel-Mixcited by 19
  11. 2024How to Benchmark Vision Foundation Models for Semantic Segmentation?cited by 15
  12. 2024Exploring the Benefits of Vision Foundation Models for Unsupervised Domain Adaptationcited by 11
  13. 2021Physics-informed Guided Disentanglement in Generative Networkscited by 9
  14. 2024Optimizing against Infeasible Inclusions from Data for Semantic Segmentation through Morphologycited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 1.

6 papers.

  1. 2025Your ViT is Secretly an Image Segmentation Modelcited by 63
  2. 2024First Place Solution to the ECCV 2024 BRAVO Challenge: Evaluating Robustness of Vision Foundation Models for Semantic Segmentationcited by 3
  3. 2025Simplifying Traffic Anomaly Detection with Video Foundation Modelscited by 3
  4. 2025What is the Added Value of UDA in the VFM Era?cited by 1
  5. 2025FindMeIfYouCan: Bringing Open Set metrics to textitnear , textitfar and textitfarther Out-of-Distribution Object Detectioncited by 0
  6. 2026U-SEG: Uncertainty in SEGmentation -- A systematic multi-variable explorationcited by 0