# Situated Cameras, Situated Knowledges: Towards an Egocentric Epistemology for Computer Vision

arXiv: https://arxiv.org/abs/2307.00064 · HTML: https://mlsanity.com/paper/2307.00064/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems](https://mlsanity.com/paper/1809.07408/) (2018)
- [Identifying First-person Camera Wearers in Third-person Videos](https://mlsanity.com/paper/1704.06340/) (2017)
- [A Survey on Recent Advances of Computer Vision Algorithms for Egocentric Video](https://mlsanity.com/paper/1501.02825/) (2015)
- [Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper](https://mlsanity.com/paper/2209.11200/) (2022)

## Built on by (most cited first, 1 of 1)

- [Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals](https://mlsanity.com/paper/2504.00860/) (2025)

## Abstract (arXiv)

In her influential 1988 paper, Situated Knowledges, Donna Haraway uses vision and perspective as a metaphor to discuss scientific knowledge. Today, egocentric computer vision discusses many of the same issues, except in a literal vision context. In this short position paper, we collapse that metaphor, and explore the interactions between feminist epistemology and egocentric CV as "Egocentric Epistemology." Using this framework, we argue for the use of qualitative, human-centric methods as a complement to performance benchmarks, to center both the literal and metaphorical perspective of human crowd workers in CV.