Anton van den Hengel

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The University of Adelaide ∙ Australian National University

Director of The Australian Institute for Machine Learning

Featured Co-authors

Chunhua Shen
235 publications
Lei Zhang
200 publications
Philip H. S. Torr
113 publications
Jian Zhang
106 publications
Ian Reid
104 publications
Peng Wang
97 publications
Svetha Venkatesh
88 publications
Jie Yang
87 publications
Chao Wang
84 publications
Lei Wang
83 publications
Hui Li
73 publications

Deep Reinforcement Learning for Unknown Anomaly Detection

We address a critical yet largely unsolved anomaly detection problem, in...
09/15/2020 ∙ by Guansong Pang, et al. ∙ 40 ∙ share

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Object-and-Action Aware Model for Visual Language Navigation

Vision-and-Language Navigation (VLN) is unique in that it requires turni...
07/29/2020 ∙ by Yuankai Qi, et al. ∙ 2 ∙ share

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Deep Learning for Anomaly Detection: A Review

Anomaly detection, a.k.a. outlier detection, has been a lasting yet acti...
07/06/2020 ∙ by Guansong Pang, et al. ∙ 59 ∙ share

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Structured Multimodal Attentions for TextVQA

Text based Visual Question Answering (TextVQA) is a recently raised chal...
06/01/2020 ∙ by Chenyu Gao, et al. ∙ 12 ∙ share

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Visual Question Answering with Prior Class Semantics

We present a novel mechanism to embed prior knowledge in a model for vis...
05/04/2020 ∙ by Violetta Shevchenko, et al. ∙ 5 ∙ share

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Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision

One of the primary challenges limiting the applicability of deep learnin...
04/20/2020 ∙ by Damien Teney, et al. ∙ 2 ∙ share

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Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection

Video anomaly detection is of critical practical importance to a variety...
03/15/2020 ∙ by Guansong Pang, et al. ∙ 1 ∙ share

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On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering

Visual Question Answering (VQA) methods have made incredible progress, b...
02/24/2020 ∙ by Xinyu Wang, et al. ∙ 12 ∙ share

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Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation

Most learning-based super-resolution (SR) methods aim to recover high-re...
01/08/2020 ∙ by Dong Gong, et al. ∙ 15 ∙ share

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Deep Anomaly Detection with Deviation Networks

Although deep learning has been applied to successfully address many dat...
11/19/2019 ∙ by Guansong, et al. ∙ 17 ∙ share

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Weakly-supervised Deep Anomaly Detection with Pairwise Relation Learning

This paper studies a rarely explored but critical anomaly detection prob...
10/30/2019 ∙ by Guansong, et al. ∙ 19 ∙ share

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On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints

The knowledge that humans hold about a problem often extends far beyond ...
09/30/2019 ∙ by Damien Teney, et al. ∙ 52 ∙ share

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V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices

One of the primary challenges faced by deep learning is the degree to wh...
07/29/2019 ∙ by Damien Teney, et al. ∙ 2 ∙ share

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An Effective Two-Branch Model-Based Deep Network for Single Image Deraining

Removing rain effects from an image automatically has many applications ...
05/14/2019 ∙ by Yinglong Wang, et al. ∙ 6 ∙ share

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Attention-guided Network for Ghost-free High Dynamic Range Imaging

Ghosting artifacts caused by moving objects or misalignments is a key ch...
04/23/2019 ∙ by Qingsen Yan, et al. ∙ 16 ∙ share

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RERERE: Remote Embodied Referring Expressions in Real indoor Environments

One of the long-term challenges of robotics is to enable humans to commu...
04/23/2019 ∙ by Yuankai Qi, et al. ∙ 8 ∙ share

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Reinforcement Learning with Attention that Works: A Self-Supervised Approach

Attention models have had a significant positive impact on deep learning...
04/06/2019 ∙ by Anthony Manchin, et al. ∙ 14 ∙ share

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Actively Seeking and Learning from Live Data

One of the key limitations of traditional machine learning methods is th...
04/05/2019 ∙ by Damien Teney, et al. ∙ 34 ∙ share

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Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

Deep autoencoder has been extensively used for anomaly detection. Traini...
04/04/2019 ∙ by Dong Gong, et al. ∙ 34 ∙ share

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What's to know? Uncertainty as a Guide to Asking Goal-oriented Questions

One of the core challenges in Visual Dialogue problems is asking the que...
12/16/2018 ∙ by Ehsan Abbasnejad, et al. ∙ 0 ∙ share

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An Active Information Seeking Model for Goal-oriented Vision-and-Language Tasks

As Computer Vision algorithms move from passive analysis of pixels to ac...
12/16/2018 ∙ by Ehsan Abbasnejad, et al. ∙ 0 ∙ share

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Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks

The task in referring expression comprehension is to localise the object...
12/12/2018 ∙ by Peng Wang, et al. ∙ 8 ∙ share

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Visual Question Answering as Reading Comprehension

Visual question answering (VQA) demands simultaneous comprehension of bo...
11/29/2018 ∙ by Hui Li, et al. ∙ 4 ∙ share

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End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging

Cardiac magnetic resonance (CMR) is used extensively in the diagnosis an...
10/23/2018 ∙ by Gerard Snaauw, et al. ∙ 4 ∙ share

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MPTV: Matching Pursuit Based Total Variation Minimization for Image Deconvolution

Total variation (TV) regularization has proven effective for a range of ...
10/12/2018 ∙ by Dong Gong, et al. ∙ 0 ∙ share

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Towards Effective Deep Embedding for Zero-Shot Learning

Zero-shot learning (ZSL) attempts to recognize visual samples of unseen ...
08/30/2018 ∙ by Lei Zhang, et al. ∙ 0 ∙ share

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Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network

Deep neural networks have achieved remarkable success in single image su...
06/05/2018 ∙ by Lei Zhang, et al. ∙ 4 ∙ share

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Learning an Optimizer for Image Deconvolution

As an integral component of blind image deblurring, non-blind deconvolut...
04/10/2018 ∙ by Dong Gong, et al. ∙ 0 ∙ share

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Real-time Semantic Image Segmentation via Spatial Sparsity

We propose an approach to semantic (image) segmentation that reduces the...
12/01/2017 ∙ by Zifeng Wu, et al. ∙ 0 ∙ share

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Visual Question Answering as a Meta Learning Task

The predominant approach to Visual Question Answering (VQA) demands that...
11/22/2017 ∙ by Damien Teney, et al. ∙ 0 ∙ share

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Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards

Despite significant progress in a variety of vision-and-language problem...
11/21/2017 ∙ by Junjie Zhang, et al. ∙ 0 ∙ share

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Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning

The Visual Dialogue task requires an agent to engage in a conversation a...
11/21/2017 ∙ by Qi Wu, et al. ∙ 0 ∙ share

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Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

A robot that can carry out a natural-language instruction has been a dre...
11/20/2017 ∙ by Peter Anderson, et al. ∙ 0 ∙ share

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Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries

Recognising objects according to a pre-defined fixed set of class labels...
11/17/2017 ∙ by Bohan Zhuang, et al. ∙ 0 ∙ share

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Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge

This paper presents a state-of-the-art model for visual question answeri...
08/09/2017 ∙ by Damien Teney, et al. ∙ 0 ∙ share

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Beyond Low Rank: A Data-Adaptive Tensor Completion Method

Low rank tensor representation underpins much of recent progress in tens...
08/03/2017 ∙ by Lei Zhang, et al. ∙ 0 ∙ share

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When Unsupervised Domain Adaptation Meets Tensor Representations

Domain adaption (DA) allows machine learning methods trained on data sam...
07/19/2017 ∙ by Hao Lu, et al. ∙ 0 ∙ share

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Visually Aligned Word Embeddings for Improving Zero-shot Learning

Zero-shot learning (ZSL) highly depends on a good semantic embedding to ...
07/18/2017 ∙ by Ruizhi Qiao, et al. ∙ 0 ∙ share

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Visual Question Answering with Memory-Augmented Networks

This paper exploits a memory-augmented neural network to predict accurat...
07/17/2017 ∙ by Chao Ma, et al. ∙ 0 ∙ share

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Bayesian Conditional Generative Adverserial Networks

Traditional GANs use a deterministic generator function (typically a neu...
06/17/2017 ∙ by Ehsan Abbasnejad, et al. ∙ 0 ∙ share

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Care about you: towards large-scale human-centric visual relationship detection

Visual relationship detection aims to capture interactions between pairs...
05/28/2017 ∙ by Bohan Zhuang, et al. ∙ 0 ∙ share

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The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions

One of the most intriguing features of the Visual Question Answering (VQ...
12/16/2016 ∙ by Peng Wang, et al. ∙ 0 ∙ share

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From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur

Removing pixel-wise heterogeneous motion blur is challenging due to the ...
12/08/2016 ∙ by Dong Gong, et al. ∙ 0 ∙ share

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Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

The trend towards increasingly deep neural networks has been driven by a...
11/30/2016 ∙ by Zifeng Wu, et al. ∙ 0 ∙ share

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Sequential Person Recognition in Photo Albums with a Recurrent Network

Recognizing the identities of people in everyday photos is still a very ...
11/30/2016 ∙ by Yao Li, et al. ∙ 0 ∙ share

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Zero-Shot Visual Question Answering

Part of the appeal of Visual Question Answering (VQA) is its promise to ...
11/17/2016 ∙ by Damien Teney, et al. ∙ 0 ∙ share

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The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs

The depth is one of the key factors behind the great success of convolut...
11/06/2016 ∙ by Yong Guo, et al. ∙ 0 ∙ share

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Graph-Structured Representations for Visual Question Answering

This paper proposes to improve visual question answering (VQA) with stru...
09/19/2016 ∙ by Damien Teney, et al. ∙ 0 ∙ share

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FVQA: Fact-based Visual Question Answering

Visual Question Answering (VQA) has attracted a lot of attention in both...
06/17/2016 ∙ by Peng Wang, et al. ∙ 0 ∙ share

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Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach

In this paper, we present an end-to-end approach to simultaneously learn...
06/06/2016 ∙ by Lin Wu, et al. ∙ 0 ∙ share

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