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arXiv Computer Vision By Tristan Gottwald, Maximilian Schier, Melanie Schaller, Bodo Rosenhahn

FLEET: Token-Based Feature Extraction for Event Camera-based Reinforcement Learning

Read the original on arXiv Computer Vision →

FLEET is a token‑based feature extractor that processes event camera data directly, using random Fourier features and cross‑attention to compress variable‑length event streams into fixed‑size latent representations. By decoupling inference cost from sensor resolution, it avoids the high compute and temporal blurring associated with CNN‑based grid aggregation. Experiments on a new high‑throughput benchmark show that FLEET outperforms state‑of‑the‑art methods and remains robust across different observation frequencies.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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