Temporal-Guided Visual Foundation Models for Event-Based Vision

• URL: http://arxiv.org/abs/2511.06238v1
• Date: Sun, 09 Nov 2025 05:45:25 GMT
• Title: Temporal-Guided Visual Foundation Models for Event-Based Vision
• Authors: Ruihao Xia, Junhong Cai, Luziwei Leng, Liuyi Wang, Chengju Liu, Ran Cheng, Yang Tang, Pan Zhou,
• Abstract summary: Event cameras offer unique advantages for vision tasks in challenging environments.<n>The potential of leveraging modern Visual Foundation Models pretrained on image data remains under-explored for event-based vision.<n>We propose TemporalGuided-FM (TGVFM), a novel framework that integrates Visual Foundation Models with our temporal context fusion.
• Score: 40.997738547677066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event cameras offer unique advantages for vision tasks in challenging environments, yet processing asynchronous event streams remains an open challenge. While existing methods rely on specialized architectures or resource-intensive training, the potential of leveraging modern Visual Foundation Models (VFMs) pretrained on image data remains under-explored for event-based vision. To address this, we propose Temporal-Guided VFM (TGVFM), a novel framework that integrates VFMs with our temporal context fusion block seamlessly to bridge this gap. Our temporal block introduces three key components: (1) Long-Range Temporal Attention to model global temporal dependencies, (2) Dual Spatiotemporal Attention for multi-scale frame correlation, and (3) Deep Feature Guidance Mechanism to fuse semantic-temporal features. By retraining event-to-video models on real-world data and leveraging transformer-based VFMs, TGVFM preserves spatiotemporal dynamics while harnessing pretrained representations. Experiments demonstrate SoTA performance across semantic segmentation, depth estimation, and object detection, with improvements of 16%, 21%, and 16% over existing methods, respectively. Overall, this work unlocks the cross-modality potential of image-based VFMs for event-based vision with temporal reasoning. Code is available at https://github.com/XiaRho/TGVFM.

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