# Can machines learn to see without visual databases?

arXiv: https://arxiv.org/abs/2110.05973 · HTML: https://mlsanity.com/paper/2110.05973/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation](https://mlsanity.com/paper/2007.04954/) (2020)
- [SAILenv: Learning in Virtual Visual Environments Made Simple](https://mlsanity.com/paper/2007.08224/) (2020)

## Built on by (most cited first, 2 of 2)

- [Collectionless Artificial Intelligence](https://mlsanity.com/paper/2309.06938/) (2023)
- [The Empirical Impact of Forgetting and Transfer in Continual Visual Odometry](https://mlsanity.com/paper/2406.01797/) (2024)

## Abstract (arXiv)

This paper sustains the position that the time has come for thinking of learning machines that conquer visual skills in a truly human-like context, where a few human-like object supervisions are given by vocal interactions and pointing aids only. This likely requires new foundations on computational processes of vision with the final purpose of involving machines in tasks of visual description by living in their own visual environment under simple man-machine linguistic interactions. The challenge consists of developing machines that learn to see without needing to handle visual databases. This might open the doors to a truly orthogonal competitive track concerning deep learning technologies for vision which does not rely on the accumulation of huge visual databases.