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arXiv · 2412.03093

Expanding Event Modality Applications through a Robust CLIP-Based Encoder

Sungheon Jeong·Hanning Chen·Sanggeon Yun·Suhyeon Cho·Wenjun Huang·Xiangjian Liu·Mohsen Imani
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Abstract

This paper introduces a powerful encoder that transfers CLIP`s capabilities to event-based data, enhancing its utility and expanding its applicability across diverse domains. While large-scale datasets have significantly advanced image-based models, the scarcity of comprehensive event datasets has limited performance potential in event modality. To address this challenge, we adapt CLIP`s architecture to align event embeddings with image embeddings, supporting zero-shot learning and preserving text alignment while mitigating catastrophic forgetting. Our encoder achieves strong performance in object recognition, with competitive results in zero-shot and few-shot learning tasks. Notably, it generalizes effectively to events extracted from video data without requiring additional training, highlighting its versatility. Additionally, we integrate this encoder within a cross-modality framework that facilitates interaction across five modalities-Image, Event, Text, Sound, and Depth-expanding the possibilities for cross-modal applications. Overall, this work underscores the transformative potential of a robust event encoder, broadening the scope and utility of event-based data across various fields.

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Sungheon Jeong, Hanning Chen, Sanggeon Yun, Suhyeon Cho, Wenjun Huang, Xiangjian Liu, Mohsen Imani. 2025-05-08. Expanding Event Modality Applications through a Robust CLIP-Based Encoder. https://arxiv.org/abs/2412.03093

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