Mask IPL: Noise-Free Intrinsic Position Learning via Computation Graph Clipping for Event-Based Spike-Driven Tracking

arXiv:2609.18716 · cs.CV · Submitted 2026-09-16 · Read on arXiv

cs.CV

Submitted: 2026-09-16

Updated: 2026-09-16

Terminology

• Transformer — 20× in this paper · explained in 2 episodes
• Data Augmentation — 8× in this paper · explained in 7 episodes
• AUC — 6× in this paper · explained in 2 episodes
• Spiking neural networks — 6× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Because Intrinsic Position Learning — 1× in this paper
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Spiking Neural Networks (SNNs) — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• TP-Spikformer: Token Pruned Spiking Transformer
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training
• How Much Position Information Do Convolutional Neural Networks Encode?
• Long-Term Visual Object Tracking with Event Cameras: An Associative Memory Augmented Tracker and A Benchmark Dataset

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