Video Action Transformer Network

@article{Girdhar2018VideoAT,
  title={Video Action Transformer Network},
  author={Rohit Girdhar and Jo{\~a}o Carreira and Carl Doersch and Andrew Zisserman},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2018},
  pages={244-253}
}
We introduce the Action Transformer model for recognizing and localizing human actions in video clips. [] Key Method We show that by using high-resolution, person-specific, class-agnostic queries, the model spontaneously learns to track individual people and to pick up on semantic context from the actions of others. Additionally its attention mechanism learns to emphasize hands and faces, which are often crucial to discriminate an action – all without explicit supervision other than boxes and class labels…

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References

SHOWING 1-10 OF 54 REFERENCES

Action Recognition using Visual Attention

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.

Attentional Pooling for Action Recognition

This work introduces a simple yet surprisingly powerful model to incorporate attention in action recognition and human object interaction tasks, and introduces a novel derivation of bottom-up and top-down attention as low-rank approximations of bilinear pooling methods (typically used for fine-grained classification).

ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification

A new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video and outperforms other baselines with comparable base architectures on HMDB51, UCF101, and Charades video classification benchmarks.

Human Action Recognition: Pose-Based Attention Draws Focus to Hands

An extensive ablation study is performed to show the strengths of this approach and the conditioning aspect of the attention mechanism and to evaluate the method on the largest currently available human action recognition dataset, NTU-RGB+D, and report state-of-the-art results.

Asynchronous Temporal Fields for Action Recognition

This work proposes a fully-connected temporal CRF model for reasoning over various aspects of activities that includes objects, actions, and intentions, where the potentials are predicted by a deep network.

VideoCapsuleNet: A Simplified Network for Action Detection

A 3D capsule network for videos, called VideoCapsuleNet: a unified network for action detection which can jointly perform pixel-wise action segmentation along with action classification, and introduces capsule-pooling in the convolutional capsule layer to address this issue which makes the voting algorithm tractable.

Human Activity Recognition with Pose-driven Attention to RGB

It is of high interest to shift the attention to different hands at different time steps depending on the activity itself, and state-of-the-art results are achieved on the largest dataset for human activity recognition, namely NTU-RGB+D.

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

I3D models considerably improve upon the state-of-the-art in action classification, reaching 80.2% on HMDB-51 and 97.9% on UCF-101 after pre-training on Kinetics, and a new Two-Stream Inflated 3D Conv net that is based on 2D ConvNet inflation is introduced.

UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

This work introduces UCF101 which is currently the largest dataset of human actions and provides baseline action recognition results on this new dataset using standard bag of words approach with overall performance of 44.5%.

The Kinetics Human Action Video Dataset

The dataset is described, the statistics are described, how it was collected, and some baseline performance figures for neural network architectures trained and tested for human action classification on this dataset are given.
...