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

Paper recorded by Signals 4 on 2026-09-16 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17

Category: cs.CV · 计算机视觉 · first seen 2026-09-17

Abstract

Spiking Neural Networks (SNNs) match the event-driven nature of event cameras and naturally extract spatiotemporal features. These properties have motivated a series of recent studies on event-based tracking with SNNs. Intrinsic Position Learning (IPL) acquires strong position information without introducing additional parameters, making it a mainstream approach for position encoding in event-base

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#26 most recent of 237 cs.CV papers we have recorded · ↑ newer: Geometry beneath the Waves: Dense Priors for Sparse-View Underwater 3D · ↓ older: RankGround: Efficient High-Resolution GUI Grounding via Lightweight Re
Cite this page: Mask IPL: Noise-Free Intrinsic Position Learning via Computation Graph Clipping for Event-Based Spike-Driven Tracking: the #26 most recent of 237 cs.CV papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/mask-ipl-noise-free-intrinsic-position-learning-via-computation-graph-clipping-f.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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