Summary of First Place Solution to the Eccv 2024 Bravo Challenge: Evaluating Robustness Of Vision Foundation Models For Semantic Segmentation, by Tommie Kerssies et al.
First Place Solution to the ECCV 2024 BRAVO Challenge: Evaluating Robustness of Vision Foundation Models for Semantic Segmentation
by Tommie Kerssies, Daan de Geus, Gijs Dubbelman
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This report presents a winning solution to the ECCV 2024 BRAVO Challenge, where a model is trained on Cityscapes and evaluated on various out-of-distribution datasets. The approach leverages powerful representations from vision foundation models by attaching a simple segmentation decoder to DINOv2 and fine-tuning the entire model. This yields better performance than more complex existing methods, securing first place in the challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a winning solution to the ECCV 2024 BRAVO Challenge. A model is trained on Cityscapes and tested on different datasets. The approach uses powerful models for vision tasks, adding a simple decoder to learn from data. This helps achieve better results than other methods. |
Keywords
» Artificial intelligence » Decoder » Fine tuning
