Event-by-event object detection on a CPU with 20x less computation

Davide Scaramuzza@davsca1

Check out our #ECCV2026 paper "Low-latency Event-based Object Detection with Spatially-Sparse Linear Attention", where we make linear attention sparse in space, recurrent in time, and parallel in training, enabling the first purely-linear-attention-based neural network for asynchronous object detection with #EventCameras, outperforming the previous best asynchronous method with 20x less computation with truly event-by-event inference on CPU! Code released!

Paper: t.co/5eLPgR9kWx
Code: t.co/3sNKrWJbVw
Video: t.co/6HL3mYOpFL

Event cameras promise extremely low-latency vision, but to fully exploit them, the neural network must be low-latency too. We introduce #SpatiallySparseLinearAttention (#SSLA) for asynchronous object detection directly from raw events.

Linear attention is particularly appealing for event cameras: it can be trained efficiently in parallel on long event sequences, while at inference it operates recurrently, updating its prediction every time a new event arrives.

The problem is that conventional linear attention updates its entire state for every event. For object detection, where fine spatial resolution matters, this quickly becomes expensive.

Our key idea is simple: an event only carries information about a small spatial region, so why update the entire spatial state? SSLA updates only the relevant parts of the state, enabling fine-grained spatial representations while keeping per-event computation low.
We achieve:
- >20× lower per-event computation than the strongest prior asynchronous baseline
- State-of-the-art accuracy among asynchronous object detection methods
- Truly event-by-event inference on CPU, designed to preserve the latency advantage of event cameras

Come to our poster on Friday September 11, 2026 from 4-6pm at ExHall #389

Reference:
Haiqing Hao, Zhipeng Sui, Rong Zou, Zijia Dai, Nikola Zubić, Davide Scaramuzza, Wenhui Wang
Low-latency Event-based Object Detection with Spatially-Sparse Linear Attention
ECCV, 2026

@Prophesee_ai @SynSenseNeuro @UZH_en
@UZH_Science @ERC_Research @UZH_ai @uzh_ifi @Tesla @BYDCompany

#EventCameras #ComputerVision #Robotics #DeepLearning #NeuromorphicVision #AI