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Found 20 papers, 4 papers with code
Controllable Video Generation by Learning the Underlying Dynamical System with Neural ODE
Videos depict the change of complex dynamical systems over time in the form of discrete image sequences.
Self-attention on Multi-Shifted Windows for Scene Segmentation
Scene segmentation in images is a fundamental yet challenging problem in visual content understanding, which is to learn a model to assign every image pixel to a categorical label.
Exploring Linear Feature Disentanglement For Neural Networks
Non-linear activation functions, e. g., Sigmoid, ReLU, and Tanh, have achieved great success in neural networks (NNs).
A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition
Firstly, we perform representation learning for audio and video modalities to obtain the semantic features of the two modalities by efficient ResNeXt and 1D CNN, respectively.
EvoGAN: An Evolutionary Computation Assisted GAN
Quantitative and Qualitative results are presented on several compound expressions, and the experimental results demonstrate the feasibility and the potential of EvoGAN.
Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations
To proactively navigate and traverse various terrains, active use of visual perception becomes indispensable.
Trajectory optimization for contact-rich motions using implicit differential dynamic programming
This paper presents a novel approach using sensitivity analysis for generalizing Differential Dynamic Programming (DDP) to systems characterized by implicit dynamics, such as those modelled via inverse dynamics and variational or implicit integrators.
Robotics Systems and Control Systems and Control
Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments
This work developed a meta-learning approach that adapts the control policy on the fly to different changing conditions for robust locomotion.
Multi-expert learning of adaptive legged locomotion
Achieving versatile robot locomotion requires motor skills which can adapt to previously unseen situations.
Field-wise Learning for Multi-field Categorical Data
We present a model that utilizes linear models with variance and low-rank constraints, to help it generalize better and reduce the number of parameters.
Distribution-aware Margin Calibration for Medical Image Segmentation
In this paper, we present a novel data-distribution-aware margin calibration method for a better generalization of the mIoU over the whole data-distribution, underpinned by a rigid lower bound.
Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks for Multimodal Marketing Intention Detection
Inspired by the fact that spreading and collecting information through the Internet becomes the norm, more and more people choose to post for-profit contents (images and texts) in social networks.
Robust High-Transparency Haptic Exploration for Dexterous Telemanipulation
Robotic teleoperation will allow us to perform complex manipulation tasks in dangerous or remote environments, such as needed for planetary exploration or nuclear decommissioning.
Robotics
Learning Pregrasp Manipulation of Objects from Ungraspable Poses
In robotic grasping, objects are often occluded in ungraspable configurations such that no pregrasp pose can be found, eg large flat boxes on the table that can only be grasped from the side.
Robotics
Reaching, Grasping and Re-grasping: Learning Fine Coordinated Motor Skills
The performance of learned policy is evaluated on three different tasks: grasping a static target, grasping a dynamic target, and re-grasping.
Robotics
Learning Whole-body Motor Skills for Humanoids
This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i. e., ankle, hip, foot tilting, and stepping strategies.
Force-guided High-precision Grasping Control of Fragile and Deformable Objects using sEMG-based Force Prediction
Regulating contact forces with high precision is crucial for grasping and manipulating fragile or deformable objects.
Robotics
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development
In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems.
Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points
In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels.
Recurrent Deterministic Policy Gradient Method for Bipedal Locomotion on Rough Terrain Challenge
This paper presents a deep learning framework that is capable of solving partially observable locomotion tasks based on our novel interpretation of Recurrent Deterministic Policy Gradient (RDPG).
