EventGeM: Global-to-Local Feature Matching for Event-Based Visual Place Recognition

arXiv:2603.05807 · cs.CV · Submitted 2026-03-06 · Read on arXiv

cs.CV

Submitted: 2026-03-06

Updated: 2026-09-18

Code: https://github.com/AdamDHines/Event-GeM

Project page: https://eventgemvpr.github.io

Terminology

• Transformer — 7× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Optical Flow — 6× in this paper · explained in 2 episodes
• Adam — 5× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Feature Extraction — 4× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 3× in this paper
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation Studies — 2× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition Workshops — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Queensland University of Technology — 2× in this paper
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• CVF International Conference on Computer Vision — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• For Simultaneous Localization And Mapping — 1× in this paper
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• Pixi: Unified Software Development and Distribution for Robotics and AI
• Deep Residual Learning for Image Recognition
• DINOv2: Learning Robust Visual Features without Supervision
• Fine-tuning CNN Image Retrieval with No Human Annotation
• Very Deep Convolutional Networks for Large-Scale Image Recognition

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