From Noise to Events: Conditional Diffusion for Event Data Augmentation

Event vision exploration holds significant achievements in various vision tasks, such as object recognition, action recognition, etc. However, improving the model’s robustness against overfitting remains a critical challenge. To address this, we propose augmenting event data from the noise rather than applying fixed data transformations. Specifically, we first decouple the quadruplet event data across spatial, temporal, and polarity dimensions to construct a representation, breaking existing frame-tied manners. Then, an event diffusion decoder is integrated with a condition extractor to gradually optimize the noise to the event data (N2E). The condition extractor aims to yield guidance vectors from the original data, which are fed into each optimization step of the diffusion process. During data augmentation, we can directly generate new event streams by adjusting the noise, diffusion parameters (e.g., number of steps or noise intensity), or conditioning vectors, thereby boosting the performance of downstream event vision models. Extensive experiments on two downstream tasks among five event vision models demonstrate that N2E achieves greater improvements than existing augmentation baselines. Code is available at https://github.com/rfww/n2e.