# Visual Memorability for Robotic Interestingness via Unsupervised Online Learning

arXiv: https://arxiv.org/abs/2005.08829 · HTML: https://mlsanity.com/paper/2005.08829/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 8 of 8)

- [AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration](https://mlsanity.com/paper/2112.01740/) (2021)
- [AirShot: Efficient Few-Shot Detection for Autonomous Exploration](https://mlsanity.com/paper/2404.05069/) (2024)
- [Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments](https://mlsanity.com/paper/2204.03140/) (2022)
- [Unsupervised Online Learning for Robotic Interestingness with Visual Memory](https://mlsanity.com/paper/2111.09793/) (2021)
- [AirObject: A Temporally Evolving Graph Embedding for Object Identification](https://mlsanity.com/paper/2111.15150/) (2021)
- [Vision-Language Memory for Spatial Reasoning](https://mlsanity.com/paper/2511.20644/) (2025)
- [Towards Continual, Online, Self-Supervised Depth](https://mlsanity.com/paper/2103.00369/) (2021)
- [A Framework for Analyzing Cross-correlators using Price's Theorem and Piecewise-Linear Decomposition](https://mlsanity.com/paper/2304.09242/) (2023)

## Abstract (arXiv)

In this paper, we explore the problem of interesting scene prediction for mobile robots. This area is currently underexplored but is crucial for many practical applications such as autonomous exploration and decision making. Inspired by industrial demands, we first propose a novel translation-invariant visual memory for recalling and identifying interesting scenes, then design a three-stage architecture of long-term, short-term, and online learning. This enables our system to learn human-like experience, environmental knowledge, and online adaption, respectively. Our approach achieves much higher accuracy than the state-of-the-art algorithms on challenging robotic interestingness datasets.