Event data classification

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (12 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Pushing the boundaries of event subsampling in event-based video classification using CNNs
  13 Sep 2024 1 repository listed
  Event cameras offer low-power visual sensing capabilities ideal for edge-device applications.
• Event Trojan: Asynchronous Event-based Backdoor Attacks
  9 Jul 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  However, research into the potential risk associated with backdoor attacks in asynchronous event data has been scarce, leaving related tasks vulnerable to potential threats.
• Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition
  8 Jun 2023 1 repository listed
  To address these issues, we propose a novel dual point-voxel absorbing graph representation learning for event stream data representation.
• Online Training Through Time for Spiking Neural Networks
  9 Oct 2022 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  With OTTT, it is the first time that two mainstream supervised SNN training methods, BPTT with SG and spike representation-based training, are connected, and meanwhile in a biologically plausible form.
• Ecsnet: Spatio-temporal feature learning for event camera
  29 Aug 2022 1 repository listed
  To fully exploit their inherent sparsity with reconciling the spatio-temporal information, we introduce a compact event representation, namely 2D-1T event cloud sequence (2D-1T ECS).
• A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks
  10 Jun 2022 1 repository listed
  Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons.
• Neuromorphic Data Augmentation for Training Spiking Neural Networks
  11 Mar 2022 1 repository listed Syntology ran 0 of 2 samples · 2 unverified
  In an effort to minimize this generalization gap, we propose Neuromorphic Data Augmentation (NDA), a family of geometric augmentations specifically designed for event-based datasets with the goal of significantly…
• Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
  27 Mar 2020 1 repository listed
  Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature.
• A predictive model for the identification of learning styles in MOOC environments
  12 Oct 2019 1 repository listed
  Massive online open course (MOOC) platform generates a large amount of data, which provides many opportunities for studying the behaviors of learners.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.