IBM's AI creates new labeled image sets using semantic content

In a paper scheduled to be presented next week during the annual Conference on Computer Vision and Pattern Recognition (CVPR), scientists at IBM, Tel Aviv University, and Technion describe a novel AI model design -- Label-Set Operations (LaSO) networks -- designed to combine pairs of labeled image examples (e.g., a pic of a dog annotated "dog" and a sheep annotated "sheep") to create new examples that incorporate the seed images' labels (a single pic of a dog and sheep annotated "dog" and "sheep"). The coauthors believe that in the future, LaSO networks could be used to augment corpora that lack sufficient real-world data. "Our method is capable of producing a sample containing … labels present in two input samples," wrote the researchers. "The proposed approach might also prove useful for the interesting visual dialog use case, where the user can manipulate the returned query results by pointing out or showing visual examples of what she [or] he likes or doesn't like." LaSO networks learn to manipulate label sets of given samples and synthesize new ones corresponding to combined label sets, taking as input photos of different types and identifying common semantic content before implicitly removing concepts present in one sample from another sample.