Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Spatiotemporal Multiplier Networks for Video Action Recognition
Spatiotemporal Residual Networks for Video Action Recognition
Deep Local Video Feature for Action Recognition
Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos
Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition
TORNADO: A Spatio-Temporal Convolutional Regression Network for Video Action Proposal
A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection
Hierarchical and Spatio-Temporal Sparse Representation for Human Action Recognition
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection
RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in Videos
Deep Moving Poselets for Video Based Action Recognition
Compressed Video Action Recognition
Evaluation of local spatio-temporal features for action recognition
Real-Time Temporal Action Localization in Untrimmed Videos by Sub-Action Discovery
Multimodal Multipart Learning for Action Recognition in Depth Videos
Modeling video evolution for action recognition
Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition
Recent advances in video-based human action recognition using deep learning: A review
Efficient Action Detection in Untrimmed Videos via Multi-task Learning
DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks
Efficient and extensible security enforcement using dynamic data flow analysis
Visualizing and Understanding Curriculum Learning for Long Short-Term Memory Networks
OWL 2: The Next Step for OWL
InputFinder: Reverse Engineering Closed Binaries using Hardware Performance Counters
Real-time object detection for "smart" vehicles