# Negative Results in Computer Vision: A Perspective

arXiv: https://arxiv.org/abs/1705.04402 · HTML: https://mlsanity.com/paper/1705.04402/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2017.

## Built on by (most cited first, 5 of 5)

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- [Stitching Gaps: Fusing Situated Perceptual Knowledge with Vision Transformers for High-Level Image Classification](https://mlsanity.com/paper/2402.19339/) (2024)
- [Conceptual Framework and Documentation Standards of Cystoscopic Media Content for Artificial Intelligence](https://mlsanity.com/paper/2301.05991/) (2023)
- [Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot Learning](https://mlsanity.com/paper/2409.02850/) (2024)

## Abstract (arXiv)

A negative result is when the outcome of an experiment or a model is not what is expected or when a hypothesis does not hold. Despite being often overlooked in the scientific community, negative results are results and they carry value. While this topic has been extensively discussed in other fields such as social sciences and biosciences, less attention has been paid to it in the computer vision community. The unique characteristics of computer vision, particularly its experimental aspect, call for a special treatment of this matter. In this paper, I will address what makes negative results important, how they should be disseminated and incentivized, and what lessons can be learned from cognitive vision research in this regard. Further, I will discuss issues such as computer vision and human vision interaction, experimental design and statistical hypothesis testing, explanatory versus predictive modeling, performance evaluation, model comparison, as well as computer vision research culture.