Experimental fields typically exhibit a strong bias towards publications with positive results, and computer vision is not an exception. However, negative or inconclusive results are considered fundamental in advancement of the science. In this workshop, we are inviting high quality negative results. Workshop on negative results in computer vision is open to any researcher studying any sub-topic of computer vision who wants to challenge current models, algorithms and datasets. We invite researchers to submit their negative results in any of the sub-topics of computer vision. Although we discourage half-baked results, we strongly believe that "negative" observations and conclusions, based on rigorous experimentation and thorough documentation, ought to be published in order to be discussed, confirmed or refuted by others. In addition, publishing well documented failures may reveal fundamental flaws and obstacles in commonly used methods, or datasets, ultimately leading to advancements in computer vision. In other other words, while being open to any area and most definitions of negative results, we expect rigorousness in the experimental study and clarity in presentation.