Papers › Event-Based Video Reconstruction Using Transformer
Event-Based Video Reconstruction Using Transformer
Wenming Weng, Yueyi Zhang, Zhiwei Xiong
Event cameras, which output events by detecting spatio-temporal brightness changes, bring a novel paradigm to image sensors with high dynamic range and low latency. Previous works have achieved impressive performances on event-based video reconstruction by introducing convolutional neural networks (CNNs). However, intrinsic locality of convolutional operations is not capable of modeling long-range dependency, which is crucial to many vision tasks. In this paper, we present a hybrid CNN-Transformer network for event-based video reconstruction (ET-Net), which merits the fine local information from CNN and global contexts from Transformer. In addition, we further propose a Token Pyramid Aggregation strategy to implement multi-scale token integration for relating internal and intersected semantic concepts in the token-space. Experimental results demonstrate that our proposed method achieves superior performance over state-of-the-art methods on multiple real-world event datasets. The code is available at https://github.com/WarranWeng/ET-Net
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Event-based Object Segmentation | DDD17-SEG | ETNet | mIoU | 0.34 | #2 of 2 | Archive leaderboard | report |
| Event-based Object Segmentation | DSEC-SEG | ETNet | mIoU | 0.36 | #2 of 2 | Archive leaderboard | report |
| Event-based Object Segmentation | MVSEC-SEG | ETNet | mIoU | 0.37 | #2 of 8 | Archive leaderboard | report |
| Event-based Object Segmentation | RGBE-SEG | ETNet | mIoU | 0.35 | #2 of 8 | Archive leaderboard | report |
| Video Reconstruction | Event-Camera Dataset | ET-Net | LPIPS | 0.224 | #2 of 4 | Archive leaderboard | report |
| Video Reconstruction | Event-Camera Dataset | ET-Net | Mean Squared Error | 0.047 | #2 of 4 | Archive leaderboard | report |
| Video Reconstruction | MVSEC | ET-Net | LPIPS | 0.489 | #2 of 4 | Archive leaderboard | report |
| Video Reconstruction | MVSEC | ET-Net | Mean Squared Error | 0.107 | #2 of 4 | 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.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections