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Found 6 papers, 5 papers with code
TODE-Trans: Transparent Object Depth Estimation with Transformer
We observe that the global characteristics of the transformer make it easier to extract contextual information to perform depth estimation of transparent areas.
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions
We demonstrate that using parallel branches as opposed to serial stacked convolutional layers will be a more powerful design for robotic visual grasping tasks.
When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection
The first key design is that we adopt the local window attention to capture local contextual information and detailed features of graspable objects.
Context-Based Soft Actor Critic for Environments with Non-stationary Dynamics
The performance of deep reinforcement learning methods prone to degenerate when applied to environments with non-stationary dynamics.
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
Deep reinforcement learning methods have shown great performance on many challenging cooperative multi-agent tasks.
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Statistical inference in massive datasets by empirical likelihood
In this paper, we propose a new statistical inference method for massive data sets, which is very simple and efficient by combining divide-and-conquer method and empirical likelihood.
