University of Technology Sydney

research

∙ 10/29/2020

Modern strategies for time series regression

This paper discusses several modern approaches to regression analysis in...

Stephanie Clark, et al. ∙

research

∙ 11/28/2019

Transform-Invariant Convolutional Neural Networks for Image Classification and Search

Convolutional neural networks (CNNs) have achieved state-of-the-art resu...

Xu Shen, et al. ∙

research

∙ 06/03/2020

Interpretable Time-series Classification on Few-shot Samples

Recent few-shot learning works focus on training a model with prior meta...

Wensi Tang, et al. ∙

research

∙ 11/19/2019

Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach

Weakly supervised semantic segmentation is a challenging task as it only...

Bingfeng Zhang, et al. ∙

research

∙ 01/19/2022

Dual Space Graph Contrastive Learning

Unsupervised graph representation learning has emerged as a powerful too...

Haoran Yang, et al. ∙

research

∙ 12/07/2019

Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development

Recently, practical applications for passenger flow prediction have brou...

Yongshun Gong, et al. ∙

research

∙ 04/08/2019

Heterogeneous Multi-task Metric Learning across Multiple Domains

Distance metric learning (DML) plays a crucial role in diverse machine l...

Yong Luo, et al. ∙

research

∙ 04/08/2019

Large Margin Multi-modal Multi-task Feature Extraction for Image Classification

The features used in many image analysis-based applications are frequent...

Yong Luo, et al. ∙

research

∙ 04/13/2019

Shakeout: A New Approach to Regularized Deep Neural Network Training

Recent years have witnessed the success of deep neural networks in deali...

Guoliang Kang, et al. ∙

research

∙ 04/17/2019

Correlated Logistic Model With Elastic Net Regularization for Multilabel Image Classification

In this paper, we present correlated logistic (CorrLog) model for multil...

Qiang Li, et al. ∙

research

∙ 10/10/2019

Searching for A Robust Neural Architecture in Four GPU Hours

Conventional neural architecture search (NAS) approaches are based on re...

Xuanyi Dong, et al. ∙

research

∙ 06/23/2020

Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation

In the unsupervised open set domain adaptation (UOSDA), the target domai...

Li Zhong, et al. ∙

research

∙ 07/22/2021

A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image

Optical Coherence Tomography Angiography (OCTA) is a non-invasive and no...

Yufei Wang, et al. ∙

research

∙ 12/16/2021

Automated Deep Learning: Neural Architecture Search Is Not the End

Deep learning (DL) has proven to be a highly effective approach for deve...

Xuanyi Dong, et al. ∙

research

∙ 11/28/2019

Continuous Dropout

Dropout has been proven to be an effective algorithm for training robust...

Xu Shen, et al. ∙

research

∙ 08/05/2020

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Artificial Intelligence (AI) has attracted a great deal of attention in ...

Tianqing Zhu, et al. ∙

research

∙ 03/22/2022

Making DeepFakes more spurious: evading deep face forgery detection via trace removal attack

DeepFakes are raising significant social concerns. Although various Deep...

Chi Liu, et al. ∙

research

∙ 08/26/2019

See More Than Once -- Kernel-Sharing Atrous Convolution for Semantic Segmentation

The state-of-the-art semantic segmentation solutions usually leverage di...

Ye Huang, et al. ∙

research

∙ 04/20/2019

Cubic LSTMs for Video Prediction

Predicting future frames in videos has become a promising direction of r...

Hehe Fan, et al. ∙

research

∙ 01/16/2020

PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting

Crowd counting, i.e., estimating the number of people in a crowded area,...

Saeed Amirgholipour, et al. ∙

research

∙ 04/13/2020

On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization

In recent years, a critical initialization scheme with orthogonal initia...

Wei Huang, et al. ∙

research

∙ 04/19/2020

Binarized Graph Neural Network

Recently, there have been some breakthroughs in graph analysis by applyi...

Hanchen Wang, et al. ∙

research

∙ 02/05/2022

Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting

Learning from unlabeled or partially labeled data to alleviate human lab...

Guofeng Mei, et al. ∙

research

∙ 12/24/2019

Unsupervised Scene Adaptation with Memory Regularization in vivo

We consider the unsupervised scene adaptation problem of learning from b...

Zhedong Zheng, et al. ∙

research

∙ 07/04/2020

RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network

Generative adversarial network (GAN) has attracted increasing attention ...

Chuan Ma, et al. ∙

research

∙ 02/22/2021

Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search

The goal of person search is to localize and match query persons from sc...

Chuchu Han, et al. ∙

research

∙ 08/05/2021

dp-GAN : Alleviating Mode Collapse in GAN via Diversity Penalty Module

The vanilla GAN [5] suffers from mode collapse deeply, which usually man...

Sen Pei, et al. ∙

research

∙ 11/20/2019

Instance-Invariant Adaptive Object Detection via Progressive Disentanglement

Most state-of-the-art methods of object detection suffer from poor gener...

Aming Wu, et al. ∙

research

∙ 12/19/2019

Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems

Context-aware recommender systems (CARS) have gained increasing attentio...

Wei Huang, et al. ∙

research

∙ 01/09/2020

Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction

Dimensionality reduction is an important operation in information visual...

Zhenhua Shi, et al. ∙

research

∙ 01/18/2021

Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions

Brain Computer Interface technologies are popular methods of communicati...

Javier Fumanal-Idocin, et al. ∙

research

∙ 07/20/2021

Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head Motion

We propose an audio-driven talking-head method to generate photo-realist...

Suzhen Wang, et al. ∙

research

∙ 12/29/2021

COTReg:Coupled Optimal Transport based Point Cloud Registration

Generating a set of high-quality correspondences or matches is one of th...

Guofeng Mei, et al. ∙

research

∙ 04/04/2019

Multi-View Intact Space Learning

It is practical to assume that an individual view is unlikely to be suff...

Chang Xu, et al. ∙

research

∙ 08/29/2019

Learning to Transfer Learn

We propose a novel framework, learning to transfer learn (L2TL), to impr...

Linchao Zhu, et al. ∙

research

∙ 03/08/2020

Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation

This paper focuses on the unsupervised domain adaptation of transferring...

Zhedong Zheng, et al. ∙

research

∙ 03/30/2020

Memory Aggregation Networks for Efficient Interactive Video Object Segmentation

Interactive video object segmentation (iVOS) aims at efficiently harvest...

Jiaxu Miao, et al. ∙

research

∙ 05/03/2020

Multi-Center Federated Learning

Federated learning has received great attention for its capability to tr...

Ming Xie, et al. ∙

research

∙ 06/29/2020

Path Integral Based Convolution and Pooling for Graph Neural Networks

Graph neural networks (GNNs) extends the functionality of traditional ne...

Zheng Ma, et al. ∙

research

∙ 09/19/2021

Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures

We propose using regularization for Multi-Agent Reinforcement Learning r...

Chapman Siu, et al. ∙

research

∙ 11/25/2021

Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting

Recent studies focus on formulating the traffic forecasting as a spatio-...

Chuanpan Zheng, et al. ∙

research

∙ 03/28/2022

Automated Progressive Learning for Efficient Training of Vision Transformers

Recent advances in vision Transformers (ViTs) have come with a voracious...

Changlin Li, et al. ∙

research

∙ 05/06/2022

BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching

In stereoscope-based Minimally Invasive Surgeries (MIS), dense stereo ma...

Jingwei Song, et al. ∙

research

∙ 05/09/2022

The Roles and Modes of Human Interactions with Automated Machine Learning Systems

As automated machine learning (AutoML) systems continue to progress in b...

Thanh Tung Khuat, et al. ∙

research

∙ 06/13/2019

Understanding Human Context in 3D Scenes by Learning Spatial Affordances with Virtual Skeleton Models

Robots are often required to operate in environments where humans are no...

Lasitha Piyathilaka, et al. ∙

research

∙ 05/28/2020

TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples

The challenges of high intra-class variance yet low inter-class fluctuat...

Huaxi Huang, et al. ∙

research

∙ 12/30/2020

How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?

Unsupervised domain adaptation (UDA) aims to train a target classifier w...

Li Zhong, et al. ∙

research

∙ 09/07/2021

Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation

User purchasing prediction with multi-behavior information remains a cha...

Haoran Yang, et al. ∙

research

∙ 05/10/2022

Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation

Predicting the duration of traffic incidents is a challenging task due t...

Artur Grigorev, et al. ∙

research

∙ 09/28/2018

Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels

It is challenging to train deep neural networks robustly on the industri...

Bo Han, et al. ∙

University of Technology Sydney

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