NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNN
This work addresses the problem of limited training data for neuromorphic vision sensors, enabling better understanding of unseen objects in this domain, though it is incremental as it adapts existing methods.
The paper tackles the challenge of recognizing unseen objects in neuromorphic data by transferring the CLIP model to neuromorphic data recognition, achieving effective performance on datasets like N-MNIST, CIFAR10-DVS, and ES-ImageNet.
Recently, the neuromorphic vision sensor has received more and more interest. However, the neuromorphic data consists of asynchronous event spikes, which makes it difficult to construct a big benchmark to train a power general neural network model, thus limiting the neuromorphic data understanding for ``unseen" objects by deep learning. While for the frame image, since the training data can be obtained easily, the zero-shot and few-shot learning for ``unseen" task via the large Contrastive Vision-Language Pre-training (CLIP) model, which is pre-trained by large-scale image-text pairs in 2D, have shown inspirational performance. We wonder whether the CLIP could be transferred to neuromorphic data recognition to handle the ``unseen" problem. To this end, we materialize this idea with NeuroCLIP in the paper. The NeuroCLIP consists of 2D CLIP and two specially designed modules for neuromorphic data understanding. First, an event-frame module that could convert the event spikes to the sequential frame image with a simple discrimination strategy. Second, an inter-timestep adapter, which is a simple fine-tuned adapter based on a spiking neural network (SNN) for the sequential features coming from the visual encoder of CLIP to improve the few-shot performance. Various experiments on neuromorphic datasets including N-MNIST, CIFAR10-DVS, and ES-ImageNet demonstrate the effectiveness of NeuroCLIP. Our code is open-sourced at https://github.com/yfguo91/NeuroCLIP.git.