# SMamba: Sparse Mamba for Event-based Object Detection

arXiv: https://arxiv.org/abs/2501.11971 · HTML: https://mlsanity.com/paper/2501.11971/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/)

## Builds on (most cited first, 10 of 10)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [A Large Scale Event-based Detection Dataset for Automotive](https://mlsanity.com/paper/2001.08499/) (2020)
- [Fusion-Mamba for Cross-modality Object Detection](https://mlsanity.com/paper/2404.09146/) (2024)
- [Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges](https://mlsanity.com/paper/2404.16112/) (2024)
- [Multi-Scale VMamba: Hierarchy in Hierarchy Visual State Space Model](https://mlsanity.com/paper/2405.14174/) (2024)
- [Visual Mamba: A Survey and New Outlooks](https://mlsanity.com/paper/2404.18861/) (2024)
- [Event-based Simultaneous Localization and Mapping: A Comprehensive Survey](https://mlsanity.com/paper/2304.09793/) (2023)

## Built on by (most cited first, 19 of 19)

- [Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios](https://mlsanity.com/paper/2508.10704/) (2025)
- [DPMambaIR: All-in-One Image Restoration via Degradation-Aware Prompt State Space Model](https://mlsanity.com/paper/2504.17732/) (2025)
- [Low-Latency Event-Based Object Detection with Spatially-Sparse Linear Attention](https://mlsanity.com/paper/2603.06228/) (2026)
- [DynamicVis: Dynamic Visual Perception for Efficient Remote Sensing Foundation Models](https://mlsanity.com/paper/2503.16426/) (2025)
- [Hyperspectral Mamba for Hyperspectral Object Tracking](https://mlsanity.com/paper/2509.08265/) (2025)
- [EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perception](https://mlsanity.com/paper/2504.16616/) (2025)
- [Focus Through Motion: RGB-Event Collaborative Token Sparsification for Efficient Object Detection](https://mlsanity.com/paper/2509.03872/) (2025)
- [SparseVoxelDet: Fully Sparse Voxel Networks for Efficient Event-Based Drone Detection](https://mlsanity.com/paper/2603.21638/) (2026)
- [Rethinking Event-Based Object Detection through Representation-Level Temporal Aggregation and Model-Level Hypergraph Reasoning](https://mlsanity.com/paper/2605.08825/) (2026)
- [Enhancing Event-based Object Detection with Monocular Normal Maps](https://mlsanity.com/paper/2508.02127/) (2025)
- [CETUS: Causal Event-Driven Temporal Modeling With Unified Variable-Rate Scheduling](https://mlsanity.com/paper/2509.13784/) (2025)
- [PSTTS: A Plug-and-Play Token Selector for Efficient Event-based Spatio-temporal Representation Learning](https://mlsanity.com/paper/2509.22481/) (2025)
- [FATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection](https://mlsanity.com/paper/2606.17334/) (2026)
- [MVPruner: Dynamic Token Pruning for Accelerating Multi-view Vision-Language Models in Autonomous Driving](https://mlsanity.com/paper/2606.27660/) (2026)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)
- [Sequence-SOD: Bio-inspired Sequence-aware Spiking ObjectDetection for Event Cameras](https://mlsanity.com/paper/2607.26703/) (2026)
- [BLADE: ReliaBle Dynamic Hardware-Aware SNN-ANN Boundary SeLection for Event-BAseD Object DEtection](https://mlsanity.com/paper/2609.17562/) (2026)
- [Bend the Clock: Predicting Ahead to Beat Latency in Event-Based Object Detection](https://mlsanity.com/paper/2609.26919/) (2026)
- [The Shape of Events: Edge-Based Inductive Biases via Cross-Domain Distillation](https://mlsanity.com/paper/2609.30478/) (2026)

## Abstract (arXiv)

Transformer-based methods have achieved remarkable performance in event-based object detection, owing to the global modeling ability. However, they neglect the influence of non-event and noisy regions and process them uniformly, leading to high computational overhead. To mitigate computation cost, some researchers propose window attention based sparsification strategies to discard unimportant regions, which sacrifices the global modeling ability and results in suboptimal performance. To achieve better trade-off between accuracy and efficiency, we propose Sparse Mamba (SMamba), which performs adaptive sparsification to reduce computational effort while maintaining global modeling capability. Specifically, a Spatio-Temporal Continuity Assessment module is proposed to measure the information content of tokens and discard uninformative ones by leveraging the spatiotemporal distribution differences between activity and noise events. Based on the assessment results, an Information-Prioritized Local Scan strategy is designed to shorten the scan distance between high-information tokens, facilitating interactions among them in the spatial dimension. Furthermore, to extend the global interaction from 2D space to 3D representations, a Global Channel Interaction module is proposed to aggregate channel information from a global spatial perspective. Results on three datasets (Gen1, 1Mpx, and eTram) demonstrate that our model outperforms other methods in both performance and efficiency.