In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation.
In this paper we present the results of our experiments in training and deploying a self-supervised retrieval-based chatbot trained with contrastive learning for assisting customer support agents.
Sophisticated trajectory prediction models that effectively mimic team dynamics have many potential uses for sports coaches, broadcasters and spectators.
This paper presents a novel adaptive fast smooth second-order sliding mode control for the attitude tracking of the three degree-of-freedom (3-DOF) helicopter system with lumped disturbances.
Systems and Control Systems and Control
Distributed deep learning systems (DDLS) train deep neural network models by utilizing the distributed resources of a cluster.
To achieve both label-free and end-to-end learning of MOT, we propose a Tracking-by-Animation framework, where a differentiable neural model first tracks objects from input frames and then animates these objects into reconstructed frames.
Automatically determining three-dimensional human pose from monocular RGB image data is a challenging problem.
Ranked #1 on 3D Human Pose Estimation on MPI-INF-3DHP
We study deep learning approaches to inferring numerical coordinates for points of interest in an input image.
Ranked #24 on Pose Estimation on MPII Human Pose
The capacity of an LSTM network can be increased by widening and adding layers.
Most research has been focused on action recognition and using it to classify many clips in continuous video for action localisation.
Due to recent advances in technology, the recording and analysis of video data has become an increasingly common component of athlete training programmes.