Core Concepts
This paper introduces state-space models (SSMs) as a novel approach to address two key challenges in event-based vision: (1) model performance degradation when operating at temporal frequencies different from training, and (2) slow training efficiency. The proposed SSM-based models demonstrate superior generalization to higher frequencies and achieve a 33% increase in training speed compared to existing recurrent and transformer-based methods.
Abstract
The paper addresses the challenges in event-based vision, particularly in the domain of object detection. It introduces state-space models (SSMs) as a novel approach to tackle two key issues:
Performance degradation when deploying models at temporal frequencies different from training:
Existing methods based on recurrent neural networks (RNNs) and transformers exhibit significant performance drops (over 20 mAP) when tested at higher frequencies than the training input.
The proposed SSM-based models, on the other hand, demonstrate minimal performance degradation (3.31 mAP drop on average) when tested at higher frequencies.
This is achieved by leveraging the learnable timescale parameter in SSMs, which allows the models to adapt to varying inference frequencies without the need for retraining.
Slow training efficiency:
Traditional RNN and transformer-based models suffer from longer training cycles due to their reliance on conventional recurrent mechanisms.
The SSM-based models, specifically the S4D and S5 variants, achieve a 33% increase in training speed compared to the state-of-the-art recurrent vision transformer (RVT) approach.
The paper also introduces two strategies to mitigate the aliasing effect encountered when deploying the models at higher frequencies:
Frequency-selective masking (bandlimiting): This approach encourages the learned convolutional kernels to be smooth, effectively suppressing high-frequency components.
H2 norm: This method attenuates the frequency response of the system after a chosen frequency, further mitigating the aliasing problem.
The comprehensive evaluation on the Gen1 and 1 Mpx event camera datasets demonstrates the effectiveness of the proposed SSM-based models in achieving competitive performance while exhibiting superior generalization and training efficiency compared to existing methods.
Stats
The paper does not provide any specific numerical data or statistics. The key insights are presented through qualitative comparisons and performance metrics such as mean Average Precision (mAP) on the Gen1 and 1 Mpx event camera datasets.
Quotes
The paper does not contain any direct quotes that are crucial to the key arguments.