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• 2020

  A high-performance spectral-spatial residual network for hyperspectral image classification with small training data

  Khotimah, W. N., Bennamoun, M., Boussaid, F., Sohel, F. & Edwards, D., Oct 2020, In : Remote Sensing. 12, 19, 3137.

  Research output: Contribution to journal › Article

  Open Access

  • image classification
  • normalization
  • spatial data
  • state of the art
  • speed

• A Survey on Deep Learning Techniques for Stereo-based Depth Estimation

  Laga, H., Jospin, L. V., Boussaid, F. & Bennamoun, M., 22 Oct 2020, In : IEEE Transactions on Pattern Analysis and Machine Intelligence. p. 1-1 28 p.

  Research output: Contribution to journal › Article

  • Depth Estimation
  • Deep learning
  • Learning
  • Community
  • Stereo Matching

• Attack to Explain Deep Representation

  Jalwana, M. A. A. K., Akhtar, N., Bennamoun, M. & Mian, A., 2020, In : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. p. 9540-9549 10 p., 9156888.

  Research output: Contribution to journal › Conference article

  • Labels
  • Classifiers
  • Deep learning
  • Casting
  • Demonstrations
  1 Citation (Scopus)

• Automatic detection of western rock lobster using synthetic data

  Mahmood, A., Bennamoun, M., An, S., Sohel, F., Boussaid, F., Hovey, R. & Kendrick, G., 1 Jul 2020, In : ICES Journal of Marine Science. 77, 4, p. 1308-1317 10 p.

  Research output: Contribution to journal › Article

  • automatic detection
  • lobster
  • lobsters
  • rocks
  • rock
  3 Citations (Scopus)

• Automatic hierarchical classification of kelps using deep residual features

  Mahmood, A., Ospina, A. G., Bennamoun, M., An, S., Sohel, F., Boussaid, F., Hovey, R., Fisher, R. B. & Kendrick, G. A., 2 Jan 2020, In : Sensors (Switzerland). 20, 2, 447.

  Research output: Contribution to journal › Article

  Open Access

  • Kelp
  • seaweeds
  • margins
  • organisms
  • Biota
  4 Citations (Scopus)

• Color vision deficiency datasets & recoloring evaluation using GANs

  Li, H., Zhang, L., Zhang, X., Zhang, M., Zhu, G., Shen, P., Li, P., Bennamoun, M. & Shah, S. A. A., 1 Oct 2020, In : Multimedia Tools and Applications. 79, 37-38

  Research output: Contribution to journal › Article

  • Color vision
  • Color
  • Screening
  • Bicycles
  • Testing

• Diffusion geometry derived keypoints and local descriptors for 3D deformable shape analysis

  Wang, X., Bennamoun, M., Sohel, F. & Lei, H., 2020, In : Journal of Circuits, Systems and Computers. 2150016X-1.

  Research output: Contribution to journal › Article

  • Geometry
  • Hot Temperature
  • Detectors
  • Mobile computing
  • Experiments
  2 Citations (Scopus)

• Guest Editors' Introduction to the Special Issue on RGB-D Vision: Methods and Applications

  Bennamoun, M., Guo, Y., Tombari, F., Youcef-Toumi, K. & Nishino, K., 1 Oct 2020, In : IEEE Transactions on Pattern Analysis and Machine Intelligence. 42, 10, p. 2329-2332 4 p., 9185968.

  Research output: Contribution to journal › Editorial

  Open Access

• Improving Image-Based Localization with Deep Learning: The Impact of the Loss Function

  Ward, I. R., Jalwana, M. A. A. K. & Bennamoun, M., 2020, Image and Video Technology - PSIVT 2019 International Workshops, Revised Selected Papers. Dabrowski, J. J., Rahman, A. & Paul, M. (eds.). Springer Link, p. 111-124 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11994 LNCS).

  Research output: Chapter in Book/Conference paper › Conference paper

  • Loss Function
  • Deep learning
  • Learning
  • Term
  • Regression

• Leveraging Linguistically-aware Object Relations and NASNet for Image Captioning

  Sharif, N., Jalwana, M. A. A. K., Bennamoun, M., Liu, W. & Shah, S. A. A., 2020, 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, Institute of Electrical and Electronics Engineers, p. 1-6 6 p.

  Research output: Chapter in Book/Conference paper › Conference paper

• Meta-transfer learning driven tensor-shot detector for the autonomous localization and recognition of concealed baggage threats

  Hassan, T., Shafay, M., Akçay, S., Khan, S., Bennamoun, M., Damiani, E. & Werghi, N., 2 Nov 2020, In : Sensors (Switzerland). 20, 22, p. 1-25 25 p., 6450.

  Research output: Contribution to journal › Article

  Open Access

  • baggage
  • Transfer (Psychology)
  • learning
  • Tensors
  • shot

• Orthogonal Deep Models as Defense against Black-Box Attacks

  Jalwana, M. A. A. K., Akhtar, N., Bennamoun, M. & Mian, A., 2020, In : IEEE Access. 8, p. 119744-119757 14 p., 9129688.

  Research output: Contribution to journal › Article

  Open Access

  • Deep learning
  • Computer vision

• Redundancy and Attention in Convolutional LSTM for Gesture Recognition

  Zhu, G., Zhang, L., Yang, L., Mei, L., Shah, S. A. A., Bennamoun, M. & Shen, P., Apr 2020, In : IEEE Transactions on Neural Networks and Learning Systems. 31, 4, p. 1323-1335 13 p., 8750878.

  Research output: Contribution to journal › Article

  • Long short-term memory
  • Gesture recognition
  • Redundancy
  • Convolution
  • Fusion reactions
  3 Citations (Scopus)

• Scale-Aware Feature Network for Weakly Supervised Semantic Segmentation

  Xu, L., Bennamoun, M., Boussaid, F. & Sohel, F., 21 Apr 2020, In : IEEE Access. 8, p. 75957-75967 11 p., 9075085.

  Research output: Contribution to journal › Article

  Open Access

  • Semantics
  • Volatile organic compounds
  • Image classification
  • Convolution
  • Image segmentation

• Structure-Feature based Graph Self-adaptive Pooling

  Zhang, L., Wang, X., Li, H., Zhu, G., Shen, P., Li, P., Lu, X., Shah, S. A. A. & Bennamoun, M., 20 Apr 2020, The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020. Association for Computing Machinery (ACM), p. 3098-3104 7 p. (The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020).

  Research output: Chapter in Book/Conference paper › Conference paper

  Open Access

  • Topology
  • Agglomeration

• Topology-learnable graph convolution for skeleton-based action recognition

  Zhu, G., Zhang, L., Li, H., Shen, P., Shah, S. A. A. & Bennamoun, M., Jul 2020, In : Pattern Recognition Letters. 135, p. 286-292 7 p.

  Research output: Contribution to journal › Article

  • Convolution
  • Topology
  • Fusion reactions
  • Deep learning
  • Set theory

• Vision to Language: Methods, Metrics and Datasets

  Sharif, N., Nadeem, U., Shah, S. A. A., Bennamoun, M. & Liu, W., 2020, Machine Learning Paradigms: Advances in Deep Learning-based Technological Applications. Tsihrintzis, G. A. & Jain, L. C. (eds.). Cham: Springer International Publishing, p. 9-62 54 p. (Learning and Analytics in Intelligent Systems; vol. 18).

  Research output: Chapter in Book/Conference paper › Chapter

  • Deep learning
  • Artificial intelligence
  • Object recognition
  • Speech recognition
  • Computer vision

• WEmbSim: A Simple yet Effective Metric for Image Captioning

  Sharif, N., White, L., Bennamoun, M., Liu, W. & Shah, S. A. A., 30 Nov 2020, Digital Image Computing: Techniques and Applications, 2020 (DICTA 2020). USA: IEEE, Institute of Electrical and Electronics Engineers

  Research output: Chapter in Book/Conference paper › Conference paper
• 2019

  3D Shape Analysis: Fundamentals, Theory, and Applications

  Laga, H., Guo, Y., Tabia, H., Fisher, R. & Bennamoun, M., 2019, Wiley-Blackwell. 368 p.

  Research output: Book/Report › Book

• A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation

  Shah, S. & Bennamoun, M., 4 Feb 2019, 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, Institute of Electrical and Electronics Engineers, 6 p. 8634657

  Research output: Chapter in Book/Conference paper › Conference paper

  • Cosmetics
  • Fillers
  • Machine learning

• An Improved Approach to Weakly Supervised Semantic Segmentation

  Xu, L., Bennamoun, M., An, S., Boussaid, F. & Sohel, F., 2019, International Conference on Acoustics, Speech, and Signal Processing (ICASSP). USA: IEEE, Institute of Electrical and Electronics Engineers, p. 1897-1901 8682788

  Research output: Chapter in Book/Conference paper › Conference paper

  • Semantics
  • Volatile organic compounds
  • Deep neural networks
  • Image segmentation
  • Labels
  2 Citations (Scopus)

• Artificial intelligence methods for real-time pharmacovigilance monitoring to predict adverse cardiac events

  Lu, J., Bennamoun, M., Sohel, F., Dwivedi, G., An, S., Rankin, J. & Sanfilippo, F., 2019, In : Heart, Lung & Circulation.

  Research output: Contribution to journal › Abstract/Meeting Abstract

• A Training-Free Mesh Upsampling and Morphing Technique for 3D Face Rejuvenation

  Shah, S., Bennamoun, M. & Molton, M., 7 Feb 2019, 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, Institute of Electrical and Electronics Engineers, 8634685. (International Conference Image and Vision Computing New Zealand; vol. 2018-November).

  Research output: Chapter in Book/Conference paper › Conference paper

  • Pixels

• Automated segmentation of gravel particles from depth images of gravel-soil mixtures

  Rahmani, H., Scanlan, C., Nadeem, U., Bennamoun, M. & Bowles, R., Jul 2019, In : Computers & Geosciences. 128, p. 1-10 10 p.

  Research output: Contribution to journal › Article

  Open Access
  3 Citations (Scopus)

• Bi-SAN-CAP: Bi-Directional Self-Attention for Image Captioning

  Hossain, M. Z., Sohel, F., Shiratuddin, M. F., Laga, H. & Bennamoun, M., 1 Dec 2019, 2019 Digital Image Computing: Techniques and Applications, DICTA 2019. IEEE, Institute of Electrical and Electronics Engineers, 8946003. (2019 Digital Image Computing: Techniques and Applications, DICTA 2019).

  Research output: Chapter in Book/Conference paper › Conference paper

  • Long short-term memory
  • Pipelines
  • Neural networks
  1 Citation (Scopus)

• Continuous Gesture Segmentation and Recognition using 3DCNN and Convolutional LSTM

  Zhu, G., Zhang, L., Shen, P., Song, J., Shah, S. A. A. & Bennamoun, M., Apr 2019, In : IEEE Transactions on Multimedia. p. 1011-1021

  Research output: Contribution to journal › Article

  • Gesture recognition
  • Long short-term memory
  • Neural networks
  • Hinges
  • Network architecture
  10 Citations (Scopus)

• Coral classification using densenet and cross-modality transfer learning

  Xu, L., Bennamoun, M., Boussaid, F., An, S. & Sohel, F., 2019, International Joint Conference on Neural Networks (IJCNN) . USA: IEEE, Institute of Electrical and Electronics Engineers

  Research output: Chapter in Book/Conference paper › Conference paper

  • Fluorescence
  • Image classification
  1 Citation (Scopus)

• Deep Fusion Net for Coral Classification in Fluorescence and Reflectance Images

  Nadeem, U., Bennamoun, M., Sohel, F. & Togneri, R., Dec 2019, 2019 Digital Image Computing: Techniques and Applications, DICTA 2019. IEEE, Institute of Electrical and Electronics Engineers, 8945925. (2019 Digital Image Computing: Techniques and Applications, DICTA 2019).

  Research output: Chapter in Book/Conference paper › Conference paper

  • Fluorescence
  • Fusion reactions
  • Reefs
  • Aquatic ecosystems
  • Lighting
  1 Citation (Scopus)

• Deep learning-based 3D local feature descriptor from Mercator projections

  Rezaei, M., Rezaeian, M., Derhami, V., Sohel, F. & Bennamoun, M., Oct 2019, In : Computer Aided Geometric Design. 74, 101771.

  Research output: Contribution to journal › Article

  • Local Features
  • Descriptors
  • Deep neural networks
  • Deep learning
  • Learning
  1 Citation (Scopus)

• Deep learning for driverless vehicles

  Hodges, C., An, S., Rahmani, H. & Bennamoun, M., 1 Jan 2019, Smart Innovation, Systems and Technologies. Springer Science + Business Media, Vol. 136. p. 83-99 17 p. (Smart Innovation, Systems and Technologies; vol. 136).

  Research output: Chapter in Book/Conference paper › Chapter

  • Deep Learning
  • Autonomous Vehicles
  • Deep learning
  • Automation
  • Autonomous vehicles
  2 Citations (Scopus)

• Deep learning for marine species recognition

  Xu, L., Bennamoun, M., An, S., Sohel, F. & Boussaid, F., 1 Jan 2019, Handbook of Deep Learning Applications. Bales, V. E., Roy, S. S., Sharma, D. & Samui, P. (eds.). USA: Springer Science + Business Media, p. 129-145 17 p. (Smart Innovation, Systems and Technologies; vol. 136).

  Research output: Chapter in Book/Conference paper › Chapter

  • Deep Learning
  • Deep learning
  • Computer Vision
  • Computer vision
  • Plankton
  6 Citations (Scopus)

• Deep learning for scene understanding

  Nadeem, U., Shah, S. A. A., Sohel, F., Togneri, R. & Bennamoun, M., 19 Mar 2019, Handbook of deep learning applications. Balas, V. E., Roy, S. S., Sharma, D. & Samui, P. (eds.). Cham: Springer, p. 21-51 27 p. (Smart Innovation, Systems and Technologies; vol. 136).

  Research output: Chapter in Book/Conference paper › Chapter

  • Deep learning
  • Computer vision
  • Neural networks
  • Network architecture
  • Computer hardware
  2 Citations (Scopus)

• Direct Image to Point Cloud Descriptors Matching for 6-DOF Camera Localization in Dense 3D Point Clouds

  Nadeem, U., Jalwana, M. A. A. K., Bennamoun, M., Togneri, R. & Sohel, F., 1 Jan 2019, Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings. Gedeon, T., Wong, K. W. & Lee, M. (eds.). Australia: Springer, p. 222-234 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11954 LNCS).

  Research output: Chapter in Book/Conference paper › Conference paper

  • Point Cloud
  • Camera
  • Descriptors
  • Cameras
  • Query

• Enhanced q-least Mean Square

  Sadiq, A., Khan, S., Naseem, I., Togneri, R. & Bennamoun, M., 26 Mar 2019, In : Circuits, Systems, and Signal Processing. 23 p.

  Research output: Contribution to journal › Article

  • Least Mean Square
  • Q-calculus
  • Learning Rate
  • Stochastic Gradient
  • Gradient Algorithm
  5 Citations (Scopus)

• Faster R-CNN Based Deep Learning for Seagrass Detection from Underwater Digital Images

  Moniruzzaman, M., Islam, S. M. S., Lavery, P. & Bennamoun, M., Dec 2019, 2019 Digital Image Computing: Techniques and Applications, DICTA 2019. IEEE, Institute of Electrical and Electronics Engineers, 8946048. (2019 Digital Image Computing: Techniques and Applications, DICTA 2019).

  Research output: Chapter in Book/Conference paper › Conference paper

  • Object detection
  • Deep learning
  • Detectors
  • Pipelines
  3 Citations (Scopus)

• Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death

  Awan, S. E., Bennamoun, M., Sohel, F., Sanfilippo, F. M., Chow, B. J. & Dwivedi, G., 26 Jun 2019, In : PLoS One. 14, 6, e0218760.

  Research output: Contribution to journal › Article

  Open Access

  • heart failure
  • artificial intelligence
  • Machine Learning
  • Feature extraction
  • Machine learning
  3 Citations (Scopus)

• Generalized joint sparse representation for multimodal biometric fusion of heterogeneous features

  Primorac, R., Togneri, R., Bennamoun, M. & Sohel, F., 30 Jan 2019, 2018 IEEE International Workshop on Information Forensics and Security (WIFS). USA: IEEE, Institute of Electrical and Electronics Engineers, 8630775

  Research output: Chapter in Book/Conference paper › Conference paper

  • Biometrics
  • Fusion reactions
  • Experiments
  1 Citation (Scopus)

• Global regularizer and temporal-aware cross-entropy for skeleton-based early action recognition

  Ke, Q., Liu, J., Bennamoun, M., Rahmani, H., An, S., Sohel, F. & Boussaid, F., 2019, Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers. Mori, G., Li, H., Schindler, K. & Jawahar, C. V. (eds.). Springer-Verlag Berlin, p. 729-745 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11364 LNCS).

  Research output: Chapter in Book/Conference paper › Conference paper

  Open Access

  • Action Recognition
  • Cross-entropy
  • Skeleton
  • Labels
  • Entropy
  3 Citations (Scopus)

• Label Universal Targeted Attack

  Akhtar, N., Jalwana, M. A. A. K., Bennamoun, M. & Mian, A., 27 May 2019, arXiv preprint 15 p.

  Research output: Contribution to specialist publication › Article

  File

  • Labels
  187 Downloads (Pure)

• LCEval: Learned Composite Metric for Caption Evaluation

  Sharif, N., White, L., Bennamoun, M., Liu, W. & Shah, S. A. A., Oct 2019, In : International Journal of Computer Vision. 127, 10, p. 1586-1610

  Research output: Contribution to journal › Article

  • Linguistics
  • Neural networks
  • Composite materials

• Learning-Based Confidence Estimation for Multi-modal Classifier Fusion

  Nadeem, U., Bennamoun, M., Sohel, F. & Togneri, R., 15 Dec 2019, Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings: part II. Gedeon, T., Wong, K. W. & Lee, M. (eds.). Australia: Springer, p. 299-312 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11954 LNCS).

  Research output: Chapter in Book/Conference paper › Conference paper

  • Classifier Fusion
  • Confidence
  • Learning
  • Fusion reactions
  • Classifiers
  1 Citation (Scopus)

• Learning Latent Global Network for Skeleton-based Action Prediction

  Ke, Q., Bennamoun, M., Rahmani, H., An, S., Sohel, F. & Boussaid, F., 2 Sep 2019, In : IEEE Transactions on Image Processing. p. 959-970

  Research output: Contribution to journal › Article

  • Lighting
  7 Citations (Scopus)

• Machine Learning Approaches for Prediction of Facial Rejuvenation Using Real and Synthetic Data

  Shah, S. A. A., Bennamoun, M. & Molton, M. K., 2019, In : IEEE Access. 7, p. 23779-23787 9 p.

  Research output: Contribution to journal › Article

  Open Access
  2 Citations (Scopus)

• Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics

  Awan, S. E., Bennamoun, M., Sohel, F., Sanfilippo, F. M. & Dwivedi, G., 1 Apr 2019, In : ESC Heart Failure. 6, 2, p. 428-435 8 p.

  Research output: Contribution to journal › Article

  Open Access

  • Machine Learning
  • Area Under Curve
  • Heart Failure
  • Neural Networks (Computer)
  • Sensitivity and Specificity
  9 Citations (Scopus)

• NormalNet: A voxel-based CNN for 3D object classification and retrieval

  Wang, C., Cheng, M., Sohel, F., Bennamoun, M. & Li, J., 5 Jan 2019, In : Neurocomputing. 323, p. 139-147 9 p.

  Research output: Contribution to journal › Article
  24 Citations (Scopus)

• Relationship detection based on object semantic inference and attention mechanisms

  Zhang, L., Zhang, S., Shen, P., Zhu, G., Shah, S. A. A. & Bennamoun, M., 5 Jun 2019, ICMR 2019 - Proceedings of the 2019 ACM International Conference on Multimedia Retrieval. USA: Association for Computing Machinery (ACM), p. 68-72 5 p. (ICMR 2019 - Proceedings of the 2019 ACM International Conference on Multimedia Retrieval).

  Research output: Chapter in Book/Conference paper › Conference paper

  Open Access

  • Semantics
  • Image understanding
  • Genes
  2 Citations (Scopus)

• ResFeats: Residual network based features for underwater image classification

  Mahmood, A., Bennamoun, M., An, S., Sohel, F. & Boussaid, F., 1 Nov 2019, In : Image and Vision Computing. 103811.

  Research output: Contribution to journal › Article

  • Aquatic ecosystems
  • Deep neural networks
  • Image classification
  • Imaging systems
  • Deep learning
  7 Citations (Scopus)

• Review of Modelling and Simulating Crowds at Mass Gathering Events: Hajj as a Case Study

  Owaidah, A., Olaru, D., Bennamoun, M., Sohel, F. & Khan, N., 31 Mar 2019, In : JASSS. 22, 2, 36 p., 9.

  Research output: Contribution to journal › Article

  Open Access
  2 Citations (Scopus)

• RGB-D Image-Based Object Detection: From Traditional Methods to Deep Learning Techniques

  Ward, I. R., Laga, H. & Bennamoun, M., 27 Oct 2019, RGB-D Image Analysis and Processing. Rosin, P. L., Lai, Y-K., Shao, L. & Liu, Y. (eds.). Netherlands: Springer, p. 169-201 33 p. (Advances in Computer Vision and Pattern Recognition).

  Research output: Chapter in Book/Conference paper › Chapter

  • Object detection
  • Deep learning
  • Computer vision
  • Availability
  • Learning algorithms

• Single image dehazing using deep neural networks

  Hodges, C., Bennamoun, M. & Rahmani, H., 1 Dec 2019, In : Pattern Recognition Letters. 128, p. 70-77 8 p.

  Research output: Contribution to journal › Article

  • Deep neural networks
  • Deep learning
  • Network performance
  • Network architecture
  • Computer vision
  9 Citations (Scopus)