Cargando…
Multi-Layer Cross Loss Model for Zero-Shot Human Activity Recognition
Most existing methods of human activity recognition are based on supervised learning. These methods can only recognize classes which appear in the training dataset, but are out of work when the classes are not in the training dataset. Zero-shot learning aims at solving this problem. In this paper, w...
Autores principales: | Wu, Tong, Chen, Yiqiang, Gu, Yang, Wang, Jiwei, Zhang, Siyu, Zhechen, Zhanghu |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206247/ http://dx.doi.org/10.1007/978-3-030-47426-3_17 |
Ejemplares similares
-
Zero-Shot Human Activity Recognition Using Non-Visual Sensors
por: Al Machot, Fadi, et al.
Publicado: (2020) -
Zero-Shot Action Recognition with Three-Stream Graph Convolutional Networks †
por: Wu, Nan, et al.
Publicado: (2021) -
Characterizing Word Embeddings for Zero-Shot Sensor-Based Human Activity Recognition
por: Matsuki, Moe, et al.
Publicado: (2019) -
Scaling Human-Object Interaction Recognition in the Video through Zero-Shot Learning
por: Maraghi, Vali Ollah, et al.
Publicado: (2021) -
Flight of the PEGASUS? Comparing Transformers on Few-Shot and Zero-Shot Multi-document Abstractive Summarization
por: Goodwin, Travis R., et al.
Publicado: (2020)