16,825 research outputs found
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization
Global covariance pooling in convolutional neural networks has achieved
impressive improvement over the classical first-order pooling. Recent works
have shown matrix square root normalization plays a central role in achieving
state-of-the-art performance. However, existing methods depend heavily on
eigendecomposition (EIG) or singular value decomposition (SVD), suffering from
inefficient training due to limited support of EIG and SVD on GPU. Towards
addressing this problem, we propose an iterative matrix square root
normalization method for fast end-to-end training of global covariance pooling
networks. At the core of our method is a meta-layer designed with loop-embedded
directed graph structure. The meta-layer consists of three consecutive
nonlinear structured layers, which perform pre-normalization, coupled matrix
iteration and post-compensation, respectively. Our method is much faster than
EIG or SVD based ones, since it involves only matrix multiplications, suitable
for parallel implementation on GPU. Moreover, the proposed network with ResNet
architecture can converge in much less epochs, further accelerating network
training. On large-scale ImageNet, we achieve competitive performance superior
to existing counterparts. By finetuning our models pre-trained on ImageNet, we
establish state-of-the-art results on three challenging fine-grained
benchmarks. The source code and network models will be available at
http://www.peihuali.org/iSQRT-COVComment: Accepted to CVPR 201
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
The paucity of videos in current action classification datasets (UCF-101 and
HMDB-51) has made it difficult to identify good video architectures, as most
methods obtain similar performance on existing small-scale benchmarks. This
paper re-evaluates state-of-the-art architectures in light of the new Kinetics
Human Action Video dataset. Kinetics has two orders of magnitude more data,
with 400 human action classes and over 400 clips per class, and is collected
from realistic, challenging YouTube videos. We provide an analysis on how
current architectures fare on the task of action classification on this dataset
and how much performance improves on the smaller benchmark datasets after
pre-training on Kinetics.
We also introduce a new Two-Stream Inflated 3D ConvNet (I3D) that is based on
2D ConvNet inflation: filters and pooling kernels of very deep image
classification ConvNets are expanded into 3D, making it possible to learn
seamless spatio-temporal feature extractors from video while leveraging
successful ImageNet architecture designs and even their parameters. We show
that, after pre-training on Kinetics, I3D models considerably improve upon the
state-of-the-art in action classification, reaching 80.9% on HMDB-51 and 98.0%
on UCF-101.Comment: Removed references to mini-kinetics dataset that was never made
publicly available and repeated all experiments on the full Kinetics datase
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