# Event Camera Data Dense Pre-training

arXiv: https://arxiv.org/abs/2311.11533 · HTML: https://mlsanity.com/paper/2311.11533/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 60 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 30 of 48)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://mlsanity.com/paper/2211.07636/) (2022)
- [Dense Contrastive Learning for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2011.09157/) (2020)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers](https://mlsanity.com/paper/2208.06366/) (2022)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [E-RAFT: Dense Optical Flow from Event Cameras](https://mlsanity.com/paper/2108.10552/) (2021)

## Built on by (most cited first, 21 of 21)

- [SpikMamba: When SNN meets Mamba in Event-based Human Action Recognition](https://mlsanity.com/paper/2410.16746/) (2024)
- [EZSR: Event-based Zero-Shot Recognition](https://mlsanity.com/paper/2407.21616/) (2024)
- [Spatially-guided Temporal Aggregation for Robust Event-RGB Optical Flow Estimation](https://mlsanity.com/paper/2501.00838/) (2025)
- [TESPEC: Temporally-Enhanced Self-Supervised Pretraining for Event Cameras](https://mlsanity.com/paper/2508.00913/) (2025)
- [Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems](https://mlsanity.com/paper/2512.24385/) (2025)
- [Generative Event Pretraining with Foundation Model Alignment](https://mlsanity.com/paper/2603.23032/) (2026)
- [ResFlow: Fine-tuning Residual Optical Flow for Event-based High Temporal Resolution Motion Estimation](https://mlsanity.com/paper/2412.09105/) (2024)
- [EAG3R: Event-Augmented 3D Geometry Estimation for Dynamic and Extreme-Lighting Scenes](https://mlsanity.com/paper/2512.00771/) (2025)
- [Perturbed State Space Feature Encoders for Optical Flow with Event Cameras](https://mlsanity.com/paper/2504.10669/) (2025)
- [Neural Ganglion Sensors: Learning Task-specific Event Cameras Inspired by the Neural Circuit of the Human Retina](https://mlsanity.com/paper/2504.13457/) (2025)
- [Maximizing Asynchronicity in Event-based Neural Networks](https://mlsanity.com/paper/2505.11165/) (2025)
- [Revealing Latent Information: A Physics-inspired Self-supervised Pre-training Framework for Noisy and Sparse Events](https://mlsanity.com/paper/2508.05507/) (2025)
- [Scaling Dense Event-Stream Pretraining from Visual Foundation Models](https://mlsanity.com/paper/2603.03969/) (2026)
- [DarkShake-DVS: Event-based Human Action Recognition under Low-light andShaking Camera Conditions](https://mlsanity.com/paper/2605.20680/) (2026)
- [EventBench: Towards Comprehensive Benchmarking of Event-based MLLMs](https://mlsanity.com/paper/2511.18448/) (2025)
- [EventGeM: Global-to-Local Feature Matching for Event-Based Visual Place Recognition](https://mlsanity.com/paper/2603.05807/) (2026)
- [Exploring deep learning for Event-Based Saliency Prediction with a Transformer-based model](https://mlsanity.com/paper/2605.23790/) (2026)
- [OctoSense: Self-Supervised Learning for Multimodal Robot Perception](https://mlsanity.com/paper/2606.27317/) (2026)
- [Hyper-RED: Scalable Event Pre-training via Semantic Hypergraph Distillation](https://mlsanity.com/paper/2609.16811/) (2026)
- [Multi-viewpoint Geo-localization with Event Cameras](https://mlsanity.com/paper/2609.21219/) (2026)
- [E-WAVE: Event-based Continuous Optical Flow via Warping-Aligned Visual Encoding](https://mlsanity.com/paper/2609.34346/) (2026)

## Abstract (arXiv)

This paper introduces a self-supervised learning framework designed for pre-training neural networks tailored to dense prediction tasks using event camera data. Our approach utilizes solely event data for training. Transferring achievements from dense RGB pre-training directly to event camera data yields subpar performance. This is attributed to the spatial sparsity inherent in an event image (converted from event data), where many pixels do not contain information. To mitigate this sparsity issue, we encode an event image into event patch features, automatically mine contextual similarity relationships among patches, group the patch features into distinctive contexts, and enforce context-to-context similarities to learn discriminative event features. For training our framework, we curate a synthetic event camera dataset featuring diverse scene and motion patterns. Transfer learning performance on downstream dense prediction tasks illustrates the superiority of our method over state-of-the-art approaches.