# AlignNet: Unsupervised Entity Alignment

arXiv: https://arxiv.org/abs/2007.08973 · HTML: https://mlsanity.com/paper/2007.08973/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos](https://mlsanity.com/paper/2205.14065/) (2022)
- [Neural Systematic Binder](https://mlsanity.com/paper/2211.01177/) (2022)
- [Generative Video Transformer: Can Objects be the Words?](https://mlsanity.com/paper/2107.09240/) (2021)
- [Structured World Belief for Reinforcement Learning in POMDP](https://mlsanity.com/paper/2107.08577/) (2021)
- [Unsupervised Object-Based Transition Models for 3D Partially Observable Environments](https://mlsanity.com/paper/2103.04693/) (2021)
- [Track, Check, Repeat: An EM Approach to Unsupervised Tracking](https://mlsanity.com/paper/2104.03424/) (2021)

## Abstract (arXiv)

Recently developed deep learning models are able to learn to segment scenes into component objects without supervision. This opens many new and exciting avenues of research, allowing agents to take objects (or entities) as inputs, rather that pixels. Unfortunately, while these models provide excellent segmentation of a single frame, they do not keep track of how objects segmented at one time-step correspond (or align) to those at a later time-step. The alignment (or correspondence) problem has impeded progress towards using object representations in downstream tasks. In this paper we take steps towards solving the alignment problem, presenting the AlignNet, an unsupervised alignment module.