6 DAILY WACV Monday Oral Presentation approach to solve this. “We decided to express the images as a set of words and then combine them in the word modality with textual domains,” he explains. “We found this was an intuitive way of tackling the problem. Specifically, for the instance-level dataset, we had to increase the number of words to describe an instance.” Retrieval is a popular computer vision task, with image-to-image search dating back to the early days of the field. Composed image retrieval represents a newer and rapidly developing area. “Specifically, it’s zero-shot composed image retrieval,” Nikos clarifies. “This is a newer task, but it’s founded on a very traditional computer vision task!” One of the most significant contributions of this research is the creation of a comprehensive testbed for domain conversion. Before this work, there was a single widely used dataset for this task, ImageNet-R. “From ImageNet-R, people were using only the photograph domain as queries, and the rest of the domains as positives and database,” Nikos tells us. “We didn’t think this was enough benchmarking for this task, so we made a testbed of four datasets.”
RkJQdWJsaXNoZXIy NTc3NzU=