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Found 10 papers, 3 papers with code
RESMatch: Referring Expression Segmentation in a Semi-Supervised Manner
This pioneering work lays the groundwork for future research in semi-supervised learning for referring expression segmentation.
D3PRefiner: A Diffusion-based Denoise Method for 3D Human Pose Refinement
Additionally, we leverage the architecture of current diffusion models to convert the distribution of noisy 3D poses into ground truth 3D poses.
Deep3DSketch+: Rapid 3D Modeling from Single Free-hand Sketches
In this work, we introduce a novel end-to-end approach, Deep3DSketch+, which performs 3D modeling using only a single free-hand sketch without inputting multiple sketches or view information.
Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation
EDH mitigates the divergence between labeled data of source subjects and unlabeled data of target subjects to accurately classify the locomotion modes of target subjects without labeling data.
How does the structure embedded in learning policy affect learning quadruped locomotion?
This paper focuses on the latter because the structured policy is more intuitive and can inherit insights from previous model-based controllers.
Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance
Many researchers have identified robotics as a potential solution to the aging population faced by many developed and developing countries.
Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)
A common way to solve this problem, known as Mixture-of-Experts, is to represent the policy as the weighted sum of multiple components, where different components perform well on different parts of the state space.
Teach Biped Robots to Walk via Gait Principles and Reinforcement Learning with Adversarial Critics
However, the local minima caused by unsuitable rewards and the overestimation of the cumulative reward impede the maximization of the cumulative reward.
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
Learning on point cloud is eagerly in demand because the point cloud is a common type of geometric data and can aid robots to understand environments robustly.
Directional PointNet: 3D Environmental Classification for Wearable Robotics
Environmental information can provide reliable prior information about human motion intent, which can aid the subject with wearable robotics to walk in complex environments.
