Response to Reviewer ij7X
**Q4:** *I think the authors still lack comparisons with recent algorithms focusing on enhancing event representation...*
**A4:** Thank you for your question! **Our approach focuses on slicing the event stream rather than proposing a new event representation method** (refer to Line 27-32 of the original manuscript). Our method is designed to be compatible with **any** event representation. In terms of comparing different event representation methods, we have already provided comparisons in Table 4 of the manuscript, which includes EST, Timesurface, and voxel grid methods. These comparisons demonstrate the effectiveness of our approach across various event representations.
As you mentioned, DVS cameras can produce uneven events due to variations in motion speed. **Our motivation is precisely to address the challenge of performance degradation caused by traditional methods that apply fixed slice to the event stream, which is independent of the age of the model.**
We have validated the effectiveness of our method through extensive experiments across a range of models and settings, including tracking, action recognition, and object recognition tasks. Additionally, following your suggestion, we have included a 2024 model within the short rebuttal period. Moreover, **in the rebuttal period**, we have supplemented with **four** new downstream tasks: lip reading, gait recognition, pose relocalization, and object detection. The summary of these experiments (**more than 100**) are summarized in the table below:
| | **Main Experiments** | | | **Ablation**| **Rebuttal Supplementary** | | | |
|:-------:|:-------:|:-------:|:------:|:------:| :------:|:------:|:------:|:------:|
| Name | Object Tracking | Action Recognition | Object Recognition| Event Representation| Lips Reading| Gait Recognition| Pose Estimation | Object Detection|
| Experiment settings | 4 backbones, 4 scenes, 3 slicing methods | 3 backbones, 2 datasets, 3 slicing methods |3 backbones, 2 datasets, 3 slicing methods | 2 tasks, 3 representation methods, 3 slicing methods | 2 backbones, 1 datasets, 2 slicing methods | 2 backbones, 1 dataset, 2 slicing methods|1 backbone, 2 datasets, 2 slicing methods | 1 self-collected dataset, 3 backbones, 2 slicing methods |
|Experiment Number | 48 | 18 | 18 |18 | 4 | 4 | 4| 6|
Our extensive experiments can support our method's effectiveness.
Thank you for sharing the information on the latest models. As the deadline is approaching, we will try our best to include the latest models in future versions. We sincerely appreciate your suggestion!
---
**Q5:** *...How does this differ from the method illustrated in Fig. 1(b)?*
**A5:** I think there might be some misunderstandings here.
First, the "number of event cells" and the "event count" in Figure 1(b) represent two **different** concepts.
Allow me to clarify the dynamic slicing process of the SNN as follows:
Suppose we have an event stream from the FE108 airplane dataset with a total duration of 24994 $\mu s$, and the SNN's inputs are 10 event cells. Each event cell (as defined in Definition 1) corresponds to an event voxel with a duration of $\delta t = \frac{24994}{10} \mu s \approx 2.5$ ms . These 10 event cells are then fed into the SNN (based on an Integrate-and-Fire neuron without decay). If a spike occurs at the 3-rd position, we extract the sub-event stream within the time interval of [0, 3*2.5] = [0, 7.5] ms. At this stage, any event representation method can be used to convert the sub-event stream into an event representation, which can then be fed into downstream tasks. Since the SNN's spike output is dynamic, the event slicing process is dynamic as well.
We hope our response helps to clarify your concerns. Thank you for your questions!