New Algorithm Follows Human Intuition to Make Visual Captioning More Grounded
Annotating and labeling datasets for machine learning problems is an expensive and time-consuming process for computer vision and natural language scientists. However, a new deep learning approach is being used to decode, localize, and reconstruct image and video captions in seconds, making the machine-generated captions more reliable and trustworthy.
To solve this problem, researchers at the Machine Learning Center at Georgia Tech (ML@GT) and Facebook have created the first cyclical algorithm that can be applied to visual captioning models. The model is able to use the three-step processing during training to make the model more visually-grounded without human annotations or introducing additional computations when deployed, saving researchers time and money on their datasets.