Update README.md

README.md

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 **Spiking Neural Network as Adaptive Event Stream Slicer (NeurIPS'24)**
 
+*Abstract:*
+Event-based cameras are attracting significant interest as they provide rich edge information, high dynamic range, and high temporal resolution. Many state-of-the-art
+event-based algorithms rely on splitting the events into fixed groups, resulting in the
+omission of crucial temporal information, particularly when dealing with diverse
+motion scenarios (e.g., high/low speed). In this work, we propose SpikeSlicer, a
+novel-designed plug-and-play event processing method capable of splitting events
+stream adaptively. SpikeSlicer utilizes a low-energy spiking neural network (SNN)
+to trigger event slicing. To guide the SNN to fire spikes at optimal time steps, we
+propose the Spiking Position-aware Loss (SPA-Loss) to modulate the neuron’s state.
+Additionally, we develop a Feedback-Update training strategy that refines the slicing decisions using feedback from the downstream artificial neural network (ANN).
+Extensive experiments demonstrate that our method yields significant performance
+improvements in event-based object tracking and recognition. Notably, SpikeSlicer
+provides a brand-new SNN-ANN cooperation paradigm, where the SNN acts as an
+efficient, low-energy data processor to assist the ANN in improving downstream
+performance, injecting new perspectives and potential avenues of exploration.
+
+<p align="left">
+    <img src="spikeslicer.gif" alt="Logo" width="38%">
+</p>
+
 More details can be found at [paper](https://arxiv.org/pdf/2410.02249) and [code](https://github.com/AndyCao1125/SpikeSlicer).
 
 We provide example Test Logs and Weights in this HF hub: