A single attention-based model trained on synthetic wide-baseline event data achieves zero-shot feature matching across unseen datasets with a reported 37.7% improvement over prior event matching methods.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
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Replacing selected attention heads in pretrained ViTs with depthwise convolutions, identified by simple strategies and recovered via fine-tuning, delivers 17-20% inference speedup on image tasks with minimal accuracy loss.
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Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras
A single attention-based model trained on synthetic wide-baseline event data achieves zero-shot feature matching across unseen datasets with a reported 37.7% improvement over prior event matching methods.
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Accelerating Vision Foundation Models with Drop-in Depthwise Convolution
Replacing selected attention heads in pretrained ViTs with depthwise convolutions, identified by simple strategies and recovered via fine-tuning, delivers 17-20% inference speedup on image tasks with minimal accuracy loss.