3 accepted papers
To tackle the challenge of recognizing images of unseen attribute-object compositions, Compositional Zero-Shot Learning (CZSL) methods have been previously addressed. However, test images in realistic scenarios may also incorporate other forms of unknown factors, such as novel semantic concepts or n…
Compositional Zero-Shot Learning (CZSL) aims at identifying unseen compositions composed of previously seen attributes and objects during the test phase. In real images, the visual appearances of attributes and objects (primitive concepts) generally interact with each other. Namely, the visual appea…
Few-shot classification aims to categorize the samples from unseen classes with only few labeled samples. To address such a challenge, many methods exploit a base set consisting of massive labeled samples to learn an instance embedding function, i.e., image feature extractor, and it is expected to p…