AdaptVPR: Route-Aware Hard Positive Generation for Robust Visual Place Recognition
AdaptVPR generates route-aware synthetic hard positives for visual place recognition, releasing the 160K-image AdaptCities dataset with R@1 gains up to 9.2% under domain shift.
AdaptVPR is a generative augmentation framework that creates same-place hard positives under illumination, weather, seasonal, and dynamic-occlusion shifts for robust visual place recognition training. A vision-language model parses scene attributes and estimates editability, while a rule-based scheduler routes generation through global appearance, local occlusion, or dual perturbation routes with geometric-consistency verification. The resulting AdaptCities dataset contains 160K verified synthetic hard positives, and experiments show R@1 gains up to 9.2% across VPR baselines and backbones. Code and data are publicly released on GitHub.
- Route-aware generation covers weather, illumination, time-of-day, and dynamic occlusion shifts
- VLM attribute parsing and rule-based scheduler choose among three generation routes
- AdaptCities dataset: 160K verified synthetic same-place hard positives
- Up to 9.2% R@1 gains across VPR baselines under domain shift
- Code and data publicly released on GitHub
- Products
- AdaptVPRAdaptCities
From the original article
1 min read at huggingface.co
Visual Place Recognition (VPR) localizes a query image by retrieving database images of the same or nearby place, yet its robustness is often degraded by domain shifts arising from illumination, weather, seasonal changes, and dynamic occlusions. One contributing factor is the limited appearance diversity of the same place in existing training data.
By Shunpeng Chen, Jingyi Zhang, Changwei Wang · huggingface.co
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