# EventTransAct: A video transformer-based framework for Event-camera based action recognition

arXiv: https://arxiv.org/abs/2308.13711 · HTML: https://mlsanity.com/paper/2308.13711/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 58 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [Video Transformer Network](https://mlsanity.com/paper/2102.00719/) (2021)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [Human Action Recognition from Various Data Modalities: A Review](https://mlsanity.com/paper/2012.11866/) (2020)
- [TCLR: Temporal Contrastive Learning for Video Representation](https://mlsanity.com/paper/2101.07974/) (2021)
- [VidTr: Video Transformer Without Convolutions](https://mlsanity.com/paper/2104.11746/) (2021)
- [EV-SegNet: Semantic Segmentation for Event-based Cameras](https://mlsanity.com/paper/1811.12039/) (2018)
- [N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras](https://mlsanity.com/paper/2112.01041/) (2021)
- [Temporal Binary Representation for Event-Based Action Recognition](https://mlsanity.com/paper/2010.08946/) (2020)

## Built on by (most cited first, 11 of 11)

- [ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action Recognition and More](https://mlsanity.com/paper/2403.12534/) (2024)
- [DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition](https://mlsanity.com/paper/2407.05106/) (2024)
- [SNN-Driven Multimodal Human Action Recognition via Sparse Spatial-Temporal Data Fusion](https://mlsanity.com/paper/2502.13385/) (2025)
- [Event-based Solutions for Human-centered Applications: A Comprehensive Review](https://mlsanity.com/paper/2502.18490/) (2025)
- [Egocentric RGB+Depth Action Recognition in Industry-Like Settings](https://mlsanity.com/paper/2309.13962/) (2023)
- [PASS: Path-selective State Space Model for Event-based Recognition](https://mlsanity.com/paper/2409.16953/) (2024)
- [Learning from Noise: Enhancing DNNs for Event-Based Vision through Controlled Noise Injection](https://mlsanity.com/paper/2506.03918/) (2025)
- [PSTTS: A Plug-and-Play Token Selector for Efficient Event-based Spatio-temporal Representation Learning](https://mlsanity.com/paper/2509.22481/) (2025)
- [CLANE: Continual Learning of Actions on Neuromorphic Hardware from Event Cameras](https://mlsanity.com/paper/2605.28387/) (2026)
- [FS-DVS: A Frequency-Selective Dynamic Visual Sensing Paradigm for Enhancing Information Completeness](https://mlsanity.com/paper/2606.06856/) (2026)
- [Event-based Action Recognition by Pre-Training on Multi-Frame Representations](https://mlsanity.com/paper/2610.13856/) (2026)

## Abstract (arXiv)

Recognizing and comprehending human actions and gestures is a crucial perception requirement for robots to interact with humans and carry out tasks in diverse domains, including service robotics, healthcare, and manufacturing. Event cameras, with their ability to capture fast-moving objects at a high temporal resolution, offer new opportunities compared to standard action recognition in RGB videos. However, previous research on event camera action recognition has primarily focused on sensor-specific network architectures and image encoding, which may not be suitable for new sensors and limit the use of recent advancements in transformer-based architectures. In this study, we employ a computationally efficient model, namely the video transformer network (VTN), which initially acquires spatial embeddings per event-frame and then utilizes a temporal self-attention mechanism. In order to better adopt the VTN for the sparse and fine-grained nature of event data, we design Event-Contrastive Loss (LEC) and event-specific augmentations. Proposed LEC promotes learning fine-grained spatial cues in the spatial backbone of VTN by contrasting temporally misaligned frames. We evaluate our method on real-world action recognition of N-EPIC Kitchens dataset, and achieve state-of-the-art results on both protocols - testing in seen kitchen (74.9% accuracy) and testing in unseen kitchens (42.43% and 46.66% Accuracy). Our approach also takes less computation time compared to competitive prior approaches, which demonstrates the potential of our framework textitEventTransAct for real-world applications of event-camera based action recognition. Project Page: urlhttps://tristandb8.github.io/EventTransActwebpage/