Add model card for Event2Vec (#1)

- Add model card for Event2Vec (c90598f53c746154add9fed3e5af7c379bc2701b)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

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+---
+pipeline_tag: video-classification
+---
+
+# Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Space
+
+Event2Vec is a novel representation that allows neural networks to process neuromorphic events directly. By drawing an analogy between words and events, this approach is fully compatible with the parallel processing capabilities of high-throughput Transformer architectures. It resolves the long-standing conflict between maintaining data sparsity and maximizing GPU efficiency, offering a promising balance for real-time, low-latency neuromorphic vision tasks.
+
+- **Paper:** [Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Space](https://huggingface.co/papers/2504.15371)
+- **Repository:** [https://github.com/Intelligent-Computing-Lab-Panda/event2vec](https://github.com/Intelligent-Computing-Lab-Panda/event2vec)
+
+## Performance
+
+The model demonstrates parameter efficiency, high throughput, and low latency across several benchmarks:
+- **DVS Gesture**
+- **ASL-DVS**
+- **DVS-Lip**
+
+It achieves high accuracy even with an extremely low number of events or low spatial resolutions.
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@misc{fang2025event2vecprocessingneuromorphicevents,
+      title={Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Space}, 
+      author={Wei Fang and Priyadarshini Panda},
+      year={2025},
+      eprint={2504.15371},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2504.15371}, 
+}
+```