Learning Visually Consistent Label Embeddings for Zero-Shot Learning

Berkan D., CİNBİŞ R. G., İKİZLER CİNBİŞ N.

2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 22 - 25 Eylül 2019 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/icip.2019.8803458
  • Basıldığı Şehir: Taipei, Taiwan
  • Anahtar Kelimeler: zero-shot learning, word embeddings, deep learning
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

In this work, we propose a zero-shot learning method to effectively model knowledge transfer between classes via jointly learning visually consistent word vectors and label embedding model in an end-to-end manner. The main idea is to project the vector space word vectors of attributes and classes into the visual space such that word representations of semantically related classes become more closer, and use the projected vectors in the proposed embedding model to identify unseen classes. We evaluate the proposed approach on two benchmark datasets and the experimental results show that our method yields significant improvements in recognition accuracy.