Lattice Long Short-Term Memory for Human Action Recognition

@article{Sun2017LatticeLS,
  title={Lattice Long Short-Term Memory for Human Action Recognition},
  author={Lin Sun and Kui Jia and Kevin Chen and Dit-Yan Yeung and Bertram E. Shi and Silvio Savarese},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={2166-2175}
}
  • Lin Sun, K. Jia, S. Savarese
  • Published 13 August 2017
  • Computer Science
  • 2017 IEEE International Conference on Computer Vision (ICCV)
Human actions captured in video sequences are threedimensional signals characterizing visual appearance and motion dynamics. [] Key Method Additionally, we introduce a novel multi-modal training procedure for training our network.

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References

SHOWING 1-10 OF 46 REFERENCES
Long-term recurrent convolutional networks for visual recognition and description
TLDR
A novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and shows such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.
Spatiotemporal Residual Networks for Video Action Recognition
TLDR
The novel spatiotemporal ResNet is introduced and evaluated using two widely used action recognition benchmarks where it exceeds the previous state-of-the-art.
Human Action Recognition Using Factorized Spatio-Temporal Convolutional Networks
TLDR
Factorized spatio-temporal convolutional networks (FstCN) are proposed that factorize the original 3D convolution kernel learning as a sequential process of learning 2D spatial kernels in the lower layers, followed by learning 1D temporal kernel in the upper layers.
Regularizing Long Short Term Memory with 3D Human-Skeleton Sequences for Action Recognition
This paper argues that large-scale action recognition in video can be greatly improved by providing an additional modality in training data - namely, 3D human-skeleton sequences - aimed at
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident.
Unsupervised Learning of Video Representations using LSTMs
TLDR
This work uses Long Short Term Memory networks to learn representations of video sequences and evaluates the representations by finetuning them for a supervised learning problem - human action recognition on the UCF-101 and HMDB-51 datasets.
Action Recognition using Visual Attention
TLDR
A soft attention based model using multi-layered Recurrent Neural Networks with Long Short-Term Memory units which are deep both spatially and temporally for action recognition in videos.
VideoLSTM convolves, attends and flows for action recognition
Two-Stream Convolutional Networks for Action Recognition in Videos
TLDR
This work proposes a two-stream ConvNet architecture which incorporates spatial and temporal networks and demonstrates that a ConvNet trained on multi-frame dense optical flow is able to achieve very good performance in spite of limited training data.
Convolutional Two-Stream Network Fusion for Video Action Recognition
TLDR
A new ConvNet architecture for spatiotemporal fusion of video snippets is proposed, and its performance on standard benchmarks where this architecture achieves state-of-the-art results is evaluated.
...
...