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, in particular its experimental aspect, calls for a special treatment of this matter. In this paper, I will address questions such as what makes negative results important, how they should be disseminated, and how they should be incentivized. Further, I will discuss issues such as computer and human vision interaction, experimental design and statistical hypothesis testing, performance evaluation and model comparison, as well as computer vision research culture. Negative Results in Computer Vision: A Perspective