Search Results for author:
Found 27 papers, 6 papers with code
Extraction and Classification of Diving Clips from Continuous Video Footage
Due to recent advances in technology, the recording and analysis of video data has become an increasingly common component of athlete training programmes.
Continuous Video to Simple Signals for Swimming Stroke Detection with Convolutional Neural Networks
Most research has been focused on action recognition and using it to classify many clips in continuous video for action localisation.
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
The capacity of an LSTM network can be increased by widening and adding layers.
Numerical Coordinate Regression with Convolutional Neural Networks
We study deep learning approaches to inferring numerical coordinates for points of interest in an input image.
Ranked #29 on
Pose Estimation
on MPII Human Pose
3D Human Pose Estimation with 2D Marginal Heatmaps
Automatically determining three-dimensional human pose from monocular RGB image data is a challenging problem.
Ranked #46 on
3D Human Pose Estimation
on MPI-INF-3DHP
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers
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.
Distributed Training of Deep Learning Models: A Taxonomic Perspective
Distributed deep learning systems (DDLS) train deep neural network models by utilizing the distributed resources of a cluster.
Adaptive Fast Smooth Second-Order Sliding Mode Control for Attitude Tracking of a 3-DOF Helicopter
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
Enhancing Trajectory Prediction using Sparse Outputs: Application to Team Sports
Sophisticated trajectory prediction models that effectively mimic team dynamics have many potential uses for sports coaches, broadcasters and spectators.
A comprehensive solution to retrieval-based chatbot construction
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.
Adaptive Smooth Disturbance Observer-Based Fast Finite-Time Attitude Tracking Control of a Small Unmanned Helicopter
In this paper, a novel adaptive smooth disturbance observer-based fast finite-time adaptive backstepping control scheme is presented for the attitude tracking of the 3-DOF helicopter system subject to compound disturbances.
Improving Sequential Recommendation Consistency with Self-Supervised Imitation
In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation.
Pose is all you need: The pose only group activity recognition system (POGARS)
We introduce a novel deep learning based group activity recognition approach called the Pose Only Group Activity Recognition System (POGARS), designed to use only tracked poses of people to predict the performed group activity.
Ranked #6 on
Group Activity Recognition
on Volleyball
Semi-supervised learning for medical image classification using imbalanced training data
Medical image classification is often challenging for two reasons: a lack of labelled examples due to expensive and time-consuming annotation protocols, and imbalanced class labels due to the relative scarcity of disease-positive individuals in the wider population.
Learning to Generate Visual Questions with Noisy Supervision
The task of visual question generation (VQG) aims to generate human-like neural questions from an image and potentially other side information (e. g., answer type or the answer itself).
Automatic Product Copywriting for E-Commerce
It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening.
Intelligent Online Selling Point Extraction for E-Commerce Recommendation
In addition, this kind of product description should be eye-catching to the readers.
Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction
At its core is a new lighting model (dubbed DSGLight) based on depth-augmented Spherical Gaussians (SG) and a Graph Convolutional Network (GCN) that infers the new lighting representation from a single LDR image of limited field-of-view.
Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce
In this paper, we proposed an automatic Scenario-based Multi-product Advertising Copywriting Generation system (SMPACG) for E-Commerce, which has been deployed on a leading Chinese e-commerce platform.
Automatic Generation of Product-Image Sequence in E-commerce
For a platform with billions of products, it is extremely time-costly and labor-expensive to manually pick and organize qualified images.
Spatial Transformer Network with Transfer Learning for Small-scale Fine-grained Skeleton-based Tai Chi Action Recognition
Human action recognition is a quite hugely investigated area where most remarkable action recognition networks usually use large-scale coarse-grained action datasets of daily human actions as inputs to state the superiority of their networks.
A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture
Agricultural research is essential for increasing food production to meet the requirements of an increasing population in the coming decades.
Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction
Recent methods mostly rely on convolutional neural networks (CNNs) to fill the missing contents in the warped panorama.
Globally Composite-Learning-Based Intelligent Fast Finite-Time Control for Uncertain Strict-Feedback Systems with Nonlinearly Periodic Disturbances
This brief aims at the issue of globally composite-learning-based neural fast finite-time (F-FnT) tracking control for a class of uncertain systems in strict-feedback form subject to nonlinearly periodic disturbances.
Classifying Whole Slide Images: What Matters?
Recently there have been many algorithms proposed for the classification of very high resolution whole slide images (WSIs).
Group Activity Recognition using Unreliable Tracked Pose
Group activity recognition in video is a complex task due to the need for a model to recognise the actions of all individuals in the video and their complex interactions.
TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations
To address such a problem, this paper proposes a novel efficient entities and relations extraction model called TDEER, which stands for Translating Decoding Schema for Joint Extraction of Entities and Relations.
Ranked #1 on
Joint Entity and Relation Extraction
on NYT
