Update README.md

README.md

@@ -5,10 +5,7 @@ license: mit
 **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
+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
@@ -21,7 +18,7 @@ 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%">
+    <img src="spikeslicer.gif" alt="Logo" width="80%">
 </p>
 
 More details can be found at [paper](https://arxiv.org/pdf/2410.02249) and [code](https://github.com/AndyCao1125/SpikeSlicer).