# Video Face Clustering with Unknown Number of Clusters

arXiv: https://arxiv.org/abs/1908.03381 · HTML: https://mlsanity.com/paper/1908.03381/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2019.

## Built on by (most cited first, 17 of 17)

- [DeepDPM: Deep Clustering With an Unknown Number of Clusters](https://mlsanity.com/paper/2203.14309/) (2022)
- [AutoAD II: The Sequel -- Who, When, and What in Movie Audio Description](https://mlsanity.com/paper/2310.06838/) (2023)
- [The One Where They Reconstructed 3D Humans and Environments in TV Shows](https://mlsanity.com/paper/2207.14279/) (2022)
- [AVA-AVD: Audio-Visual Speaker Diarization in the Wild](https://mlsanity.com/paper/2111.14448/) (2021)
- [Face, Body, Voice: Video Person-Clustering with Multiple Modalities](https://mlsanity.com/paper/2105.09939/) (2021)
- [Learning Discriminative Representations and Decision Boundaries for Open Intent Detection](https://mlsanity.com/paper/2203.05823/) (2022)
- [Contextual AD Narration with Interleaved Multimodal Sequence](https://mlsanity.com/paper/2403.12922/) (2024)
- [HYPO: Hyperspherical Out-of-Distribution Generalization](https://mlsanity.com/paper/2402.07785/) (2024)
- [Neural Clustering based Visual Representation Learning](https://mlsanity.com/paper/2403.17409/) (2024)
- [MICap: A Unified Model for Identity-aware Movie Descriptions](https://mlsanity.com/paper/2405.11483/) (2024)
- [An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders](https://mlsanity.com/paper/2406.02465/) (2024)
- [APES: Audiovisual Person Search in Untrimmed Video](https://mlsanity.com/paper/2106.01667/) (2021)
- [Self-supervised Video-centralised Transformer for Video Face Clustering](https://mlsanity.com/paper/2203.13166/) (2022)
- [Efficient Large-Scale Face Clustering Using an Online Mixture of Gaussians](https://mlsanity.com/paper/2103.17272/) (2021)
- [Self-Contained Entity Discovery from Captioned Videos](https://mlsanity.com/paper/2208.06662/) (2022)
- [VideoClusterNet: Self-Supervised and Adaptive Face Clustering For Videos](https://mlsanity.com/paper/2407.12214/) (2024)
- [Unified and Dynamic Graph for Temporal Character Grouping in Long Videos](https://mlsanity.com/paper/2308.14105/) (2023)

## Abstract (arXiv)

Understanding videos such as TV series and movies requires analyzing who the characters are and what they are doing. We address the challenging problem of clustering face tracks based on their identity. Different from previous work in this area, we choose to operate in a realistic and difficult setting where: (i) the number of characters is not known a priori; and (ii) face tracks belonging to minor or background characters are not discarded. To this end, we propose Ball Cluster Learning (BCL), a supervised approach to carve the embedding space into balls of equal size, one for each cluster. The learned ball radius is easily translated to a stopping criterion for iterative merging algorithms. This gives BCL the ability to estimate the number of clusters as well as their assignment, achieving promising results on commonly used datasets. We also present a thorough discussion of how existing metric learning literature can be adapted for this task.