Anton van den Hengel
Anton van den Hengel
Director, Australian Institute for Machine Learning, UoA | Director of Applied Science, Amazon
Vahvistettu sähköpostiosoite verkkotunnuksessa adelaide.edu.au - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
Image-based recommendations on styles and substitutes
J McAuley, C Targett, Q Shi, A Van Den Hengel
Proceedings of the 38th international ACM SIGIR conference on research and …, 2015
14062015
Efficient piecewise training of deep structured models for semantic segmentation
G Lin, C Shen, A Van Den Hengel, I Reid
Proceedings of the IEEE conference on computer vision and pattern …, 2016
9332016
A survey of appearance models in visual object tracking
X Li, W Hu, C Shen, Z Zhang, A Dick, AVD Hengel
ACM transactions on Intelligent Systems and Technology (TIST) 4 (4), 1-48, 2013
8312013
Wider or deeper: Revisiting the resnet model for visual recognition
Z Wu, C Shen, A Van Den Hengel
Pattern Recognition 90, 119-133, 2019
7692019
Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments
P Anderson, Q Wu, D Teney, J Bruce, M Johnson, N Sünderhauf, I Reid, ...
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
5782018
Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs
B Li, C Shen, Y Dai, A Van Den Hengel, M He
Proceedings of the IEEE conference on computer vision and pattern …, 2015
4932015
Learning to rank in person re-identification with metric ensembles
S Paisitkriangkrai, C Shen, A Van Den Hengel
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015
4922015
What value do explicit high level concepts have in vision to language problems?
Q Wu, C Shen, L Liu, A Dick, A Van Den Hengel
Proceedings of the IEEE conference on computer vision and pattern …, 2016
4272016
Fast supervised hashing with decision trees for high-dimensional data
G Lin, C Shen, Q Shi, A Van den Hengel, D Suter
Proceedings of the IEEE conference on computer vision and pattern …, 2014
4202014
Is face recognition really a compressive sensing problem?
Q Shi, A Eriksson, A Van Den Hengel, C Shen
CVPR 2011, 553-560, 2011
3422011
Ask me anything: Free-form visual question answering based on knowledge from external sources
Q Wu, P Wang, C Shen, A Dick, A Van Den Hengel
Proceedings of the IEEE conference on computer vision and pattern …, 2016
3322016
Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection
D Gong, L Liu, V Le, B Saha, MR Mansour, S Venkatesh, A Hengel
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
3262019
Tips and tricks for visual question answering: Learnings from the 2017 challenge
D Teney, P Anderson, X He, A Van Den Hengel
Proceedings of the IEEE conference on computer vision and pattern …, 2018
3132018
Efficient computation of robust weighted low-rank matrix approximations using the L_1 norm
A Eriksson, A Van Den Hengel
IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (9), 1681-1690, 2012
292*2012
On the fitting of surfaces to data with covariances
W Chojnacki, MJ Brooks, A Van Den Hengel, D Gawley
IEEE Transactions on pattern analysis and machine intelligence 22 (11), 1294 …, 2000
2702000
Image captioning and visual question answering based on attributes and external knowledge
Q Wu, C Shen, P Wang, A Dick, A Van Den Hengel
IEEE transactions on pattern analysis and machine intelligence 40 (6), 1367-1381, 2017
2692017
Graph-structured representations for visual question answering
D Teney, L Liu, A van Den Hengel
Proceedings of the IEEE conference on computer vision and pattern …, 2017
2682017
Videotrace: rapid interactive scene modelling from video
A Van Den Hengel, A Dick, T Thormählen, B Ward, PHS Torr
ACM Transactions on Graphics (ToG) 26 (3), 86-es, 2007
2682007
Visual question answering: A survey of methods and datasets
Q Wu, D Teney, P Wang, C Shen, A Dick, A van den Hengel
Computer Vision and Image Understanding 163, 21-40, 2017
2582017
From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur
D Gong, J Yang, L Liu, Y Zhang, I Reid, C Shen, A Van Den Hengel, Q Shi
Proceedings of the IEEE conference on computer vision and pattern …, 2017
2582017
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