# Intel Labs at Ego4D Challenge 2022: A Better Baseline for Audio-Visual Diarization

arXiv: https://arxiv.org/abs/2210.07764 · HTML: https://mlsanity.com/paper/2210.07764/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection](https://mlsanity.com/paper/2107.06592/) (2021)
- [AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection](https://mlsanity.com/paper/1901.01342/) (2019)
- [Learning Long-Term Spatial-Temporal Graphs for Active Speaker Detection](https://mlsanity.com/paper/2207.07783/) (2022)
- [Deep Lip Reading: a comparison of models and an online application](https://mlsanity.com/paper/1806.06053/) (2018)
- [AVA-AVD: Audio-Visual Speaker Diarization in the Wild](https://mlsanity.com/paper/2111.14448/) (2021)
- [Advances in Online Audio-Visual Meeting Transcription](https://mlsanity.com/paper/1912.04979/) (2019)
- [Who said that?: Audio-visual speaker diarisation of real-world meetings](https://mlsanity.com/paper/1906.10042/) (2019)

## Built on by (most cited first, 9 of 9)

- [Action Scene Graphs for Long-Form Understanding of Egocentric Videos](https://mlsanity.com/paper/2312.03391/) (2023)
- [LoCoNet: Long-Short Context Network for Active Speaker Detection](https://mlsanity.com/paper/2301.08237/) (2023)
- [STHG: Spatial-Temporal Heterogeneous Graph Learning for Advanced Audio-Visual Diarization](https://mlsanity.com/paper/2306.10608/) (2023)
- [Audio-Visual Activity Guided Cross-Modal Identity Association for Active Speaker Detection](https://mlsanity.com/paper/2212.00539/) (2022)
- [VideoSAGE: Video Summarization with Graph Representation Learning](https://mlsanity.com/paper/2404.10539/) (2024)
- [GateFusion: Hierarchical Gated Cross-Modal Fusion for Active Speaker Detection](https://mlsanity.com/paper/2512.15707/) (2025)
- [Graph-Based Multimodal and Multi-view Alignment for Keystep Recognition](https://mlsanity.com/paper/2501.04121/) (2025)
- [Keystep Recognition using Graph Neural Networks](https://mlsanity.com/paper/2506.01102/) (2025)
- [EgoVIS@CVPR: PAIR-Net: Enhancing Egocentric Speaker Detection via Pretrained Audio-Visual Fusion and Alignment Loss](https://mlsanity.com/paper/2506.02247/) (2025)

## Abstract (arXiv)

This report describes our approach for the Audio-Visual Diarization (AVD) task of the Ego4D Challenge 2022. Specifically, we present multiple technical improvements over the official baselines. First, we improve the detection performance of the camera wearer's voice activity by modifying the training scheme of its model. Second, we discover that an off-the-shelf voice activity detection model can effectively remove false positives when it is applied solely to the camera wearer's voice activities. Lastly, we show that better active speaker detection leads to a better AVD outcome. Our final method obtains 65.9% DER on the test set of Ego4D, which significantly outperforms all the baselines. Our submission achieved 1st place in the Ego4D Challenge 2022.