Relation
Quantitative Findings of the Sports-1M video classification experiments using CNNs and feature histogram baseline models
- As for the CNN Models (single frame/ early fusion/ late fusion/ slow fusion), they learn well compared to the baseline model. The single-frame model already displays strong performance.
- Compared to the wide gap relative to the feature-based baseline, the variation among different CNN architectures are comparatively insignificant.
- Multiresolution CNN architectures are 2-4 times faster due to reduced dimensions from input layer.
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Updated 2021-08-19
Tags
Deep Learning
Data Science
Related
Time Information Fusion in CNNs
Multiresolution CNNs
Quantitative Findings of the Sports-1M video classification experiments using CNNs and feature histogram baseline models
Difference between single frame network and slow fusion (motion-aware) networks
Training CNN Models for Sports-1M Video Classification
Datasets used for Experimentation
Transfer Learning Experiments on UCF-101