Recent advances in 2D zero-shot and few-shot recognition often leverage large
pre-trained vision-language models (VLMs) such as CLIP. Due to a shortage of
suitable datasets, it is currently infeasible to train such models for event
camera data. Thus, leveraging existing models across modalities is an important
research challenge. In this work, we propose EventCLIP, a new method that
utilizes CLIP for zero-shot and few-shot recognition on event camera data.
First, we demonstrate the suitability of CLIP's image embeddings for zero-shot
event classification by converting raw events to 2D grid-based representations.
Second, we propose a feature adapter that aggregates temporal information over
event frames and refines text embeddings to better align with the visual
inputs. We evaluate our work on N-Caltech, N-Cars, and N-ImageNet datasets
under the few-shot learning setting, where EventCLIP achieves state-of-the-art
performance. Finally, we show that the robustness of existing event-based
classifiers against data variations can be further boosted by ensembling with
EventCLIP.

本研究提出了一种新的方法：EventCLIP，利用 2D zero-shot recognition 和 few-shot learning 来识别 event camera data，并且使用 temporal feature adapter 来提高模型性能。通过实验证明，EventCLIP 在不同数据集上表现出 state-of-the-art performance，并且能够提高现有的 event-based classifiers 对数据变化的鲁棒性。