GEP transfers semantic knowledge from image foundation models to event data via alignment and generative pretraining on mixed sequences to create transferable event-based visual models.
Eventbind: Learning a unified representation to bind them all for event-based open-world understanding
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RE-VLM fuses RGB and event data in a dual-stream VLM with a graph-based pipeline for generating training captions and QA pairs, plus two new datasets, showing gains over RGB-only and event-only baselines especially in challenging conditions.
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Generative Event Pretraining with Foundation Model Alignment
GEP transfers semantic knowledge from image foundation models to event data via alignment and generative pretraining on mixed sequences to create transferable event-based visual models.
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RE-VLM: Event-Augmented Vision-Language Model for Scene Understanding
RE-VLM fuses RGB and event data in a dual-stream VLM with a graph-based pipeline for generating training captions and QA pairs, plus two new datasets, showing gains over RGB-only and event-only baselines especially in challenging conditions.