E-WAVE: Event-based Continuous Optical Flow via Warping-Aligned Visual Encoding

arXiv:2609.34346 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Optical Flow — 71× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 8× in this paper
• Attention Mechanism — 7× in this paper · explained in 3 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Transformer — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Iterative Refinement — 4× in this paper · explained in 3 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Angular Error — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition Workshops — 1× in this paper
• Decode — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Energy Efficiency — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• IEEE International Conference on Computer Vision — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Spiking neural networks — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• CoWTracker: Tracking by Warping instead of Correlation
• Decoupled Weight Decay Regularization
• Masked Depth Modeling for Spatial Perception
• WAFT: Warping-Alone Field Transforms for Optical Flow

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