Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: Modeling Attention in Panoramic Video: A Deep Reinforcement Learning Approach.
Recurrent Models of Visual Attention
Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition
Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition
Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering
Multimodal Multipart Learning for Action Recognition in Depth Videos
Modeling Sub-Document Attention Using Viewport Time
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
An Object-Based Bayesian Framework for Top-Down Visual Attention
Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition
Video Question Answering via Hierarchical Dual-Level Attention Network Learning
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
Human Visual Search as a Deep Reinforcement Learning Solution to a POMDP.
Deep Visual Attention Prediction.
RATM: Recurrent Attentive Tracking Model
Learning to Decompose and Disentangle Representations for Video Prediction
Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning
Visual Tracking Using Attention-Modulated Disintegration and Integration
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
Residual attention-based LSTM for video captioning
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning
Predicting the likelihood of heart failure with a multi level risk assessment using decision tree
Insider Threats to Cloud Computing: Directions for New Research Challenges
Structured Pruning of Deep Convolutional Neural Networks
numerical algorithms based on analytic function values at roots of unity .
Playing Atari with Deep Reinforcement Learning