Papers › End-to-End Learning of Representations for Asynchronous Event-Based Data
End-to-End Learning of Representations for Asynchronous Event-Based Data
Daniel Gehrig, Antonio Loquercio, Konstantinos G. Derpanis, Davide Scaramuzza
Event cameras are vision sensors that record asynchronous streams of per-pixel brightness changes, referred to as "events". They have appealing advantages over frame-based cameras for computer vision, including high temporal resolution, high dynamic range, and no motion blur. Due to the sparse, non-uniform spatiotemporal layout of the event signal, pattern recognition algorithms typically aggregate events into a grid-based representation and subsequently process it by a standard vision pipeline, e.g., Convolutional Neural Network (CNN). In this work, we introduce a general framework to convert event streams into grid-based representations through a sequence of differentiable operations. Our framework comes with two main advantages: (i) allows learning the input event representation together with the task dedicated network in an end to end manner, and (ii) lays out a taxonomy that unifies the majority of extant event representations in the literature and identifies novel ones. Empirically, we show that our approach to learning the event representation end-to-end yields an improvement of approximately 12% on optical flow estimation and object recognition over state-of-the-art methods.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Classification | N-CARS | ResNet34 + EST | Accuracy (%) | 92.5 | #3 of 6 | Archive leaderboard | report |
| Classification | N-CARS | ResNet34 + EST | Architecture | CNN | #3 of 6 | Archive leaderboard | report |
| Classification | N-CARS | ResNet34 + EST | Inference Time | 6.47 | #3 of 6 | Archive leaderboard | report |
| Classification | N-CARS | ResNet34 + EST | Params (M) | 21.8 | #3 of 6 | Archive leaderboard | report |
| Classification | N-CARS | ResNet34 + EST | Representation | EST | #3 of 6 | Archive leaderboard | report |
| Classification | N-CARS | ResNet34 + EST | Representation Time( ms / 100ms events) | 0.38 | #3 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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