SSLA-Det researchers present low-latency event-based object detection at ECCV 2026; reduces per-event computation by over 20x
This paper has been accepted for publication at the European Conference on Computer Vision (ECCV), 2026 Low-latency Event-based Object Detection with Spatially-Sparse Linear Attention Haiqing Hao1 , Zhipeng Sui1 , Rong Zou2 , Zijia Dai3 , Nikola Zubić2 , Davide Scaramuzza2 , and Wenhui Wang1⋆ 1 State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China 2 Robotics and Perception Group, University of Zurich, Zurich, Switzerland 3 ShanghaiTech University, Shanghai, China Abstract. Event cameras provide sequential visual data with spatial sparsity and high temporal resolution, making them attractive for low- latency object detection. Existing asynchronous event-based neural net- works exploit this low-latency advantage by updating predictions event by event, but still suffer from two bottlenecks: recurrent architectures are difficult to train efficiently on long sequences, and improving accu- racy often increases per-event computation and latency. Linear atten- tion is appealing because it enables parallel training and recurrent infer- ence. However, its dense state updates make per-event computation scale with the state size, yielding a poor accuracy-efficiency trade-off for ob- ject detection, where accurate localization requires fine-grained spatial states. The key challenge is therefore to introduce sparse state activa- tion that exploits the spatial sparsity of events while preserving efficient parallel training. We propose Spatially-Sparse Linear Attention (SSLA), which introduces a mixture-of-spaces state decomposition and a scatter- compute-gather training procedure, enabling state-level sparsity as well as training parallelism. Building on SSLA, we develop an end-to-end asynchronous linear attention model, SSLA-Det, for low-latency event- based object detection. On Gen1 and N-Caltech101, SSLA-Det achieves state-of-the-art accuracy among asynchronous methods, reaching 0.375 mAP and 0.515 mAP, respectively, while reducing per-event computation by over 20\times compared with the strongest prior asynchronous baseline, demonstrating the potential of linear attention for low-latency event- based vision. Code is available at: https://github.com/haohq19/ssla. Keywords: Event camera · Linear attention · Object detection 1 Introduction Event cameras provide sequential visual data with spatial sparsity and high temporal resolution, making them highly promising for low-latency perception [9,11,29]. Asynchronous event-based neural networks realize this potential by up- dating their predictions every time a new event arrives [36,37]. This event-driven ⋆Corresponding author: wwh@tsinghua.edu.cn
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