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EventCLIP: Adapting CLIP for Event-based Object Recognition
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Recent advances in zero-shot and few-shot classification heavily rely on the success of pre-trained vision-language models (VLMs) such as CLIP. Due to a shortage of large-scale datasets, training such models for event camera data remains infeasible. Thus, adapting existing VLMs across modalities to event vision is an important research challenge. In this work, we introduce EventCLIP, a novel approach that utilizes CLIP for zero-shot and few-shot event-based object recognition. We first generalize CLIP's image encoder to event data by converting raw events to 2D grid-based representations. To further enhance performance, we propose a feature adapter to aggregate temporal information over event frames and refine text embeddings to better align with the visual inputs. We evaluate EventCLIP on N-Caltech, N-Cars, and N-ImageNet datasets, achieving state-of-the-art few-shot performance. When fine-tuned on the entire dataset, our method outperforms all existing event classifiers. Moreover, we explore practical applications of EventCLIP including robust event classification and label-free event recognition, where our approach surpasses previous baselines designed specifically for these tasks.
Forward citations
Cited by 7 Pith papers
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EventPrune: Cascaded Event-Assisted Token Pruning for Efficient First-Person Dynamic Spatial Reasoning
EventPrune prunes 80% of visual tokens in Video-LLMs using event camera motion cues, yielding 1.89x speedup, 52% fewer GFLOPs, and slightly higher accuracy than full-token baselines on first-person dynamic spatial reasoning.
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Ego-Human Motion Prediction with 3D-Aware LLM
Ego3DLM jointly predicts past and future 3D body pose and motion descriptions in a single autoregressive pass, conditioned on egocentric video, 3D scene features, and three-point tracking, achieving state-of-the-art o...
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EventDrive: Event Cameras for Vision-Language Driving Intelligence
EventDrive supplies a multi-task benchmark and EventDrive-VLM architecture that fuses event data, RGB, and language supervision, reporting gains in temporal precision and motion awareness for driving intelligence.
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RE-VLM: Event-Augmented Vision-Language Model for Scene Understanding
RE-VLM is the first dual-stream VLM combining RGB and event data with a graph-based pipeline to generate training captions and QA pairs, showing gains over RGB-only and event-only models on new datasets for challengin...
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EventFace: Event-Based Face Recognition via Structure-Driven Spatiotemporal Modeling
EventFace achieves 94.19% Rank-1 accuracy and 5.35% EER on a new small event-based face dataset by transferring facial structure priors via LoRA and fusing them with temporal motion features.
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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...
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