A single event-camera feature matching model trained on synthetic data achieves zero-shot wide-baseline correspondence across unseen datasets with 37.7% improvement over prior methods.
Roth, and Daguang Xu
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cs.CV 2years
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DeltaSeg, a tiered-attention U-Net variant with a novel Deep Delta Attention module, outperforms 12 prior models on two multi-class structural defect segmentation benchmarks.
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Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras
A single event-camera feature matching model trained on synthetic data achieves zero-shot wide-baseline correspondence across unseen datasets with 37.7% improvement over prior methods.
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DeltaSeg: Tiered Attention and Deep Delta Learning for Multi-Class Structural Defect Segmentation
DeltaSeg, a tiered-attention U-Net variant with a novel Deep Delta Attention module, outperforms 12 prior models on two multi-class structural defect segmentation benchmarks.