We have developed algorithms that significantly speed up standard annotations for Computer Vision through human-in-the-loop AI models.
Using recurrent and convolutional neural networks, the algorithms assist human annotators through both annotation creation and editing, while simultaneously learning to continuously improve from human assistance.
These state-of-the-art algorithms are fully integrated in the Toronto Annotation Platform, a web-based application that allows for larger, more efficient, and more accurate data annotation projects.
The project is led by Prof. Sanja Fidler, an computer vision and AI pioneer whose research interests include 2D and 3D object detection, particularly scalable multi-class detection, object segmentation and image labeling, and (3D) scene understanding. Her work also focuses on the interplay between language and vision: generating sentential descriptions about complex scenes, as well as using textual descriptions for better scene parsing (e.g., in the scenario of the human-robot interaction).
Modern Machine Learning heavily depends on large scale annotated data, which is still primarily connected through fully manual human labour. Our product significantly outperforms annotation benchmarks in both automatic (10% absolute and 16% relative improvement in mean IoU) and interactive modes (requiring 50% fewer clicks by annotators). In simulated settings, we show a dramatic reduction of >10x number of human clicks required to annotate data to high levels of accuracy.
Multiple patent applications have been filed for the inventions that are part of this opportunity.