Cargando…
Multiobject Tracking of Wildlife in Videos Using Few-Shot Learning
SIMPLE SUMMARY: Video recordings enable scientists to estimate species’ presence, richness, abundance, demography, and activity. The increasing popularity of camera traps has led to a growing interest in developing approaches to more efficiently process images. Advanced artificial intelligence syste...
Autores principales: | Feng, Jiangfan, Xiao, Xinxin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099723/ https://www.ncbi.nlm.nih.gov/pubmed/35565649 http://dx.doi.org/10.3390/ani12091223 |
Ejemplares similares
-
FewJoint: few-shot learning for joint dialogue understanding
por: Hou, Yutai, et al.
Publicado: (2022) -
Causal Factor Disentanglement for Few-Shot Domain Adaptation in Video Prediction
por: Cornille, Nathan, et al.
Publicado: (2023) -
Learning few-shot imitation as cultural transmission
por: Bhoopchand, Avishkar, et al.
Publicado: (2023) -
Visual Object Tracking Algorithm Based on Biological Visual Information Features and Few-Shot Learning
por: Zhang, Dawei, et al.
Publicado: (2022) -
Efficient few-shot machine learning for classification of EBSD patterns
por: Kaufmann, Kevin, et al.
Publicado: (2021)