In UX research, it is crucial to build products with a clear objective that meets a specific need of a well-defined target audience. However, within the context of self-driving cars (SDCs) research, this principle appears to be largely neglected. SDC research seems to have the goal of creating cars that can drive anywhere, at any […]
The IR systems performed very poorly for both object detection and labeling. Cuy was not recognized at all across four images.
The IR systems performed poorly in detecting as well as labeling one meal. The name of the dish nor any of its ingredients were correctly identified.
Finally, the IR systems performed somewhat well! Though object detection was still lacking, several systems correctly labeled the meal in two different pictures.
The four IR systems performed better than in previous analyses, but still made quite some mistakes.
An overview of all the countries’ results from the project Recognizing Food from around the World.
Overall, the systems performed very poorly. Not a single item was correctly detected and severe cultural misrepresentations were made.
The systems performed poorly. No images were correctly described, nor were any items in the images correctly detected.
Overall, the systems’ performances were disappointing. “Bread” appeared somewhat easy to detect, though “Soup” was not.
Overall, the systems’ performances were disappointing. In one image, “Rice” was labeled by multiple systems (though not detected), but not in the other where the rice was much more visible.