Web: http://arxiv.org/abs/2209.10304

Sept. 22, 2022, 1:14 a.m. | Muhammad Ferjad Naeem, Yongqin Xian, Luc Van Gool, Federico Tombari

cs.CV updates on arXiv.org arxiv.org

Despite the tremendous progress in zero-shot learning(ZSL), the majority of
existing methods still rely on human-annotated attributes, which are difficult
to annotate and scale. An unsupervised alternative is to represent each class
using the word embedding associated with its semantic class name. However, word
embeddings extracted from pre-trained language models do not necessarily
capture visual similarities, resulting in poor zero-shot performance. In this
work, we argue that online textual documents, e.g., Wikipedia, contain rich
visual descriptions about object classes, therefore …

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