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Found 12 papers, 3 papers with code
Human Action Recognition Based on Spatial-Temporal Attention
Many state-of-the-art methods of recognizing human action are based on attention mechanism, which shows the importance of attention mechanism in action recognition.
MBDF-Net: Multi-Branch Deep Fusion Network for 3D Object Detection
Fusing the two kinds of data usually helps to improve the detection results.
Probabilistic Human Motion Prediction via A Bayesian Neural Network
The experimental results show that our approach makes the interaction more efficient and safer.
REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter
We collect the annotations of object poses, segmentations, grasps, and relationships in each image for comprehensive perception of grasping.
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation
General methods for policy based multi-agent reinforcement learning to solve the challenge introduce differentiate value functions or advantage functions for individual agents.
A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning
Using a gating mechanism that discriminates the unseen samples from the seen samples can decompose the GZSL problem to a conventional Zero-Shot Learning (ZSL) problem and a supervised classification problem.
REGNet: REgion-based Grasp Network for End-to-end Grasp Detection in Point Clouds
Reliable robotic grasping in unstructured environments is a crucial but challenging task.
Robotics
Hindsight Trust Region Policy Optimization
We propose \emph{Hindsight Trust Region Policy Optimization}(HTRPO), a new RL algorithm that extends the highly successful TRPO algorithm with \emph{hindsight} to tackle the challenge of sparse rewards.
A Multi-task Convolutional Neural Network for Autonomous Robotic Grasping in Object Stacking Scenes
Autonomous robotic grasping plays an important role in intelligent robotics.
Robotics
A Real-time Robotic Grasp Approach with Oriented Anchor Box
The main component of our approach is a grasp detection network with oriented anchor boxes as detection priors.
Robotics
ROI-based Robotic Grasp Detection for Object Overlapping Scenes
Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset.
Robotics
Fully Convolutional Grasp Detection Network with Oriented Anchor Box
The feature extractor is a deep convolutional neural network.