# Automated Video Labelling: Identifying Faces by Corroborative Evidence

arXiv: https://arxiv.org/abs/2102.05645 · HTML: https://mlsanity.com/paper/2102.05645/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [In defence of metric learning for speaker recognition](https://mlsanity.com/paper/2003.11982/) (2020)
- [Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval](https://mlsanity.com/paper/2007.12163/) (2020)
- [Condensed Movies: Story Based Retrieval with Contextual Embeddings](https://mlsanity.com/paper/2005.04208/) (2020)
- [ASR is all you need: cross-modal distillation for lip reading](https://mlsanity.com/paper/1911.12747/) (2019)
- [PixelLink: Detecting Scene Text via Instance Segmentation](https://mlsanity.com/paper/1801.01315/) (2018)
- [Perfect match: Improved cross-modal embeddings for audio-visual synchronisation](https://mlsanity.com/paper/1809.08001/) (2018)
- [From Benedict Cumberbatch to Sherlock Holmes: Character Identification in TV series without a Script](https://mlsanity.com/paper/1801.10442/) (2018)
- [Playing a Part: Speaker Verification at the Movies](https://mlsanity.com/paper/2010.15716/) (2020)

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

- [AutoAD: Movie Description in Context](https://mlsanity.com/paper/2303.16899/) (2023)
- [AutoAD II: The Sequel -- Who, When, and What in Movie Audio Description](https://mlsanity.com/paper/2310.06838/) (2023)
- [Face, Body, Voice: Video Person-Clustering with Multiple Modalities](https://mlsanity.com/paper/2105.09939/) (2021)
- [Look, Listen and Recognise: Character-Aware Audio-Visual Subtitling](https://mlsanity.com/paper/2401.12039/) (2024)
- [How you feelin'? Learning Emotions and Mental States in Movie Scenes](https://mlsanity.com/paper/2304.05634/) (2023)
- [Audio Description Generation in the Era of LLMs and VLMs: A Review of Transferable Generative AI Technologies](https://mlsanity.com/paper/2410.08860/) (2024)
- [FocusedAD: Character-centric Movie Audio Description](https://mlsanity.com/paper/2504.12157/) (2025)
- [Character-aware audio-visual subtitling in context](https://mlsanity.com/paper/2410.11068/) (2024)
- [Self-Contained Entity Discovery from Captioned Videos](https://mlsanity.com/paper/2208.06662/) (2022)

## Abstract (arXiv)

We present a method for automatically labelling all faces in video archives, such as TV broadcasts, by combining multiple evidence sources and multiple modalities (visual and audio). We target the problem of ever-growing online video archives, where an effective, scalable indexing solution cannot require a user to provide manual annotation or supervision. To this end, we make three key contributions: (1) We provide a novel, simple, method for determining if a person is famous or not using image-search engines. In turn this enables a face-identity model to be built reliably and robustly, and used for high precision automatic labelling; (2) We show that even for less-famous people, image-search engines can then be used for corroborative evidence to accurately label faces that are named in the scene or the speech; (3) Finally, we quantitatively demonstrate the benefits of our approach on different video domains and test settings, such as TV shows and news broadcasts. Our method works across three disparate datasets without any explicit domain adaptation, and sets new state-of-the-art results on all the public benchmarks.