· 2014
Large-Scale Video Classification with Convolutional Neural Networks
- 6,357citations
- 2014year
Short summary
Convolutional Neural Networks (CNNs) achieved 63.9% accuracy on a 1 million video dataset, outperforming strong baselines by 8.6% but showing only a modest 1.6% gain over single-frame models.
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Key points
- CNNs were evaluated on a 1 million video dataset with 487 classes.
- Spatio-temporal CNNs improved accuracy to 63.9% from 55.3% compared to baselines.
- Performance improvement over single-frame models was modest (59.3% to 60.9%).
- Retraining on UCF-101 dataset yielded significant gains (63.3% vs. 43.9%).
AI-generated from the title and abstract; the full text is not read.
Abstract
Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes. We study multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggest a multiresolution, foveated architecture as a promising way of speeding up the training. Our best spatio-temporal networks display significant performance improvements compared to strong feature-based baselines (55.3% to 63.9%), but only a surprisingly modest improvement compared to single-frame models (59.3% to 60.9%). We further study the generalization performance of our best model by retraining the top layers on the UCF-101 Action Recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63.3% up from 43.9%).
The authors' abstract, as published at the source., 2014 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science