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arXiv · 2611.01558

Text-Assisted Event Stream Visual Place Recognition via Multi-Scale Spatiotemporal Optimal Transport

Lan Chen·Xingxing Xiong·Xiao Wang·Ziyan Zhang·Zikang Yan·Yuanhao Zheng·Bo Jiang·Yonghong Tian
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Abstract

Benefiting from the high dynamic range, high temporal resolution, and low energy consumption of event cameras, low-altitude economic research leveraging event cameras has become a popular topic. Several existing attempts introduce natural-language descriptions into visual place recognition (VPR) to mitigate the sparsity of structural spatial information inherent to event cameras. Nevertheless, these methods still fail to fully address two core challenges: effectively encoding event streams and semantically aligning event streams with text. In this paper, we propose a multi-scale spatial-temporal optimal transport approach, MSTOT-EPR, to address these challenges. Specifically, given event streams and scene text information, we first adopt a visual encoder and a text encoder to extract respective feature representations. We then apply optimal-transport constraints to achieve cross-modal alignment. We then design a cross-modal bottleneck fusion module to further fuse bimodal features. Furthermore, we develop multi-scale spatial optimal transport and inter-frame temporal optimal transport modules to mine high-quality spatial-temporal event features for scene recognition. Extensive experiments on the NYC-Event-VPR and EPRBench datasets fully validate the effectiveness of our proposed framework for event-based VPR. The source code of this paper will be released upon acceptance.

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Lan Chen, Xingxing Xiong, Xiao Wang, Ziyan Zhang, Zikang Yan, Yuanhao Zheng, Bo Jiang, Yonghong Tian. 2026-11-01. Text-Assisted Event Stream Visual Place Recognition via Multi-Scale Spatiotemporal Optimal Transport. https://arxiv.org/abs/2611.01558

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