# Spatiotemporal Attention Learning Framework for Event-Driven Object Recognition

arXiv: https://arxiv.org/abs/2504.00370 · HTML: https://mlsanity.com/paper/2504.00370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [End-to-End Learning of Representations for Asynchronous Event-Based Data](https://mlsanity.com/paper/1904.08245/) (2019)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [EV-SegNet: Semantic Segmentation for Event-based Cameras](https://mlsanity.com/paper/1811.12039/) (2018)
- [Time-Ordered Recent Event (TORE) Volumes for Event Cameras](https://mlsanity.com/paper/2103.06108/) (2021)
- [EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction](https://mlsanity.com/paper/2305.00434/) (2023)

## Abstract (arXiv)

Event-based vision sensors, inspired by biological neural systems, asynchronously capture local pixel-level intensity changes as a sparse event stream containing position, polarity, and timestamp information. These neuromorphic sensors offer significant advantages in dynamic range, latency, and power efficiency. Their working principle inherently addresses traditional camera limitations such as motion blur and redundant background information, making them particularly suitable for dynamic vision tasks. While recent works have proposed increasingly complex event-based architectures, the computational overhead and parameter complexity of these approaches limit their practical deployment. This paper presents a novel spatiotemporal learning framework for event-based object recognition, utilizing a VGG network enhanced with Convolutional Block Attention Module (CBAM). Our approach achieves comparable performance to state-of-the-art ResNet-based methods while reducing parameter count by 2.3% compared to the original VGG model. Specifically, it outperforms ResNet-based methods like MVF-Net, achieving the highest Top-1 accuracy of 76.4% (pretrained) and 71.3% (not pretrained) on CIFAR10-DVS, and 72.4% (not pretrained) on N-Caltech101. These results highlight the robustness of our method when pretrained weights are not used, making it suitable for scenarios where transfer learning is unavailable. Moreover, our approach reduces reliance on data augmentation. Experimental results on standard event-based datasets demonstrate the framework's efficiency and effectiveness for real-world applications.