Negative results in computer vision: A perspective

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 manuscript, 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 matters such as experimental design, statistical hypothesis testing, explanatory versus predictive modeling, performance evaluation, model comparison, reproducibility of findings, the confluence of computer vision and human vision, as well as computer vision research culture.