EventCLIP: Adapting CLIP for Event-based Object Recognition

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.