|Member Login

Publications

2019 Submitted [57]

Multi-modal Ensemble Classification for Generalized Zero Shot Learning

Felix, R., Sasdelli, M., Reid, I., & Carneiro, G. (2019). Multi-modal Ensemble Classification for Generalized Zero Shot Learning. Retrieved from https://arxiv.org/pdf/1901.04623

View more

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

Liu, Y., Liu, L., Rezatofighi, H., Do, T.-T., Shi, Q., & Reid, I. (2019). Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes. Retrieved from https://arxiv.org/pdf/1901.03796

View more

A Practical Maximum Clique Algorithm for Matching with Pairwise Constraints

Constraints´alvaro, C., Bustos, P., Chin, T.-J., Neumann, F., Friedrich, T., & Katzmann, M. (2019). A Practical Maximum Clique Algorithm for Matching with Pairwise Constraints´Alvaro. Retrieved from https://arxiv.org/pdf/1902.01534.pdf

View more

Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. Retrieved from https://arxiv.org/pdf/1902.09630

View more

RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion

Li, J., Liu, Y., Gong, D., Shi, Q., Yuan, X., Zhao, C., & Reid, I. (2019). RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion. Retrieved from https://arxiv.org/pdf/1903.00620

View more

Associatively Segmenting Instances and Semantics in Point Clouds

Wang, X., Liu, S., Shen, X., Shen, C., & Jia, J. (2019). Associatively Segmenting Instances and Semantics in Point Clouds. Retrieved from https://github.com/WXinlong/ASIS.

View more

Self-supervised Learning for Single View Depth and Surface Normal Estimation

Zhan, H., Saroj Weerasekera, C., Garg, R., & Reid, I. (2019). Self-supervised Learning for Single View Depth and Surface Normal Estimation. Retrieved from https://arxiv.org/pdf/1903.00112

View more

CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning

Zhang, C., Lin, G., Liu, F., Yao, R., & Shen, C. (2019). CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning. Retrieved from https://arxiv.org/pdf/1903.02351

View more

Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation

Tian, Z., He, T., Shen, C., & Yan, Y. (2019). Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation *. Retrieved from https://arxiv.org/pdf/1903.02120

View more

Knowledge Adaptation for Efficient Semantic Segmentation

He, T., Shen, C., Tian, Z., Gong, D., Sun, C., & Yan, Y. (2019). Knowledge Adaptation for Efficient Semantic Segmentation *. Retrieved from https://arxiv.org/pdf/1903.04688

View more

Semi-and Weakly Supervised Directional Bootstrapping Model for Automated Skin Lesion Segmentation

Xie, Y., Zhang, J., Xia, Y., & Shen, C. (2019). Semi-and Weakly Supervised Directional Bootstrapping Model for Automated Skin Lesion Segmentation. Retrieved from https://arxiv.org/pdf/1903.03313

View more

Training Quantized Network with Auxiliary Gradient Module

Zhuang, B., Liu, L., Tan, M., Shen, C., & Reid, I. (2019). Training Quantized Network with Auxiliary Gradient Module. Retrieved from https://arxiv.org/pdf/1903.11236.pdf

View more

Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis

Guo, Y., Chen, Q., Chen, J., Wu, Q., Shi, Q., & Tan, M. (2019). Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis. Retrieved from https://arxiv.org/pdf/1903.11250

View more

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

Gong, D., Liu, L., Le, V., Saha, B., Mansour, R., Venkatesh, S., & Van Den Hengel, A. (2019). Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection. Retrieved from https://donggong1.github.io/anomdec-memae

View more

Actively Seeking and Learning from Live Data

Teney, D., & Van Den Hengel, A. (2019). Actively Seeking and Learning from Live Data. Retrieved from https://arxiv.org/pdf/1904.02865

View more

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

Manchin, A., Abbasnejad, E., & Van Den Hengel, A. (2019). Reinforcement Learning with Attention that Works: A Self-Supervised Approach. Retrieved from https://arxiv.org/pdf/1904.03367

View more

Architecture Search of Dynamic Cells for Semantic Video Segmentation

Nekrasov, V., Chen, H., Shen, C., & Reid, I. (2019). Architecture Search of Dynamic Cells for Semantic Video Segmentation. Retrieved from https://arxiv.org/pdf/1904.02371

View more

FCOS: Fully Convolutional One-Stage Object Detection

Tian, Z., Shen, C., Chen, H., & He, T. (2019). FCOS: Fully Convolutional One-Stage Object Detection. Retrieved from https://arxiv.org/pdf/1904.01355

View more

Template-Based Automatic Search of Compact Semantic Segmentation Architectures

Nekrasov, V., Shen, C., & Reid, I. (2019). Template-Based Automatic Search of Compact Semantic Segmentation Architectures. Retrieved from https://arxiv.org/pdf/1904.02365

View more

A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning

Do, T.-T., Tran, T., Reid, I., Kumar, V., Hoang, T., & Carneiro, G. (2019). A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning. Retrieved from https://arxiv.org/pdf/1904.08720

View more

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

Yan, Q., Gong, D., Shi, Q., van den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2019). Attention-guided Network for Ghost-free High Dynamic Range Imaging. Retrieved from https://donggong1.github.io/ahdr

View more

V2CNet: A Deep Learning Framework to Translate Videos to Commands for Robotic Manipulation

Nguyen, A., Do, T.-T., Reid, I., Caldwell, D. G., & Tsagarakis, N. G. (2019). V2CNet: A Deep Learning Framework to Translate Videos to Commands for Robotic Manipulation. Retrieved from http://arxiv.org/abs/1903.10869

View more

Bayesian Generative Active Deep Learning

Tran, T., Do, T.-T., Reid, I., & Carneiro, G. (2019). Bayesian Generative Active Deep Learning. Retrieved from https://arxiv.org/pdf/1904.11643

View more

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

Wang, Y., Gong, D., Yang, J., Shi, Q., Hengel, A. van den, Xie, D., & Zeng, B. (2019). An Effective Two-Branch Model-Based Deep Network for Single Image Deraining. Retrieved from http://arxiv.org/abs/1905.05404

View more

Event-Based Motion Segmentation by Motion Compensation

Stoffregen, T., Gallego, G., Drummond, T., Kleeman, L., & Scaramuzza, D. (2019). Event-Based Motion Segmentation by Motion Compensation. Retrieved from http://arxiv.org/abs/1904.01293

View more

CED: Color Event Camera Dataset

Scheerlinck, C., Rebecq, H., Stoffregen, T., Barnes, N., Mahony, R., & Scaramuzza, D. (2019). CED: Color Event Camera Dataset. Retrieved from https://arxiv.org/pdf/1904.10772

View more

Learning to Engage with Interactive Systems: A Field Study

Meng, L., Lin, D., Francey, A., Gorbet, R., Beesley, P., & Kuli´c, D. K. (2019). LEARNING TO ENGAGE WITH INTERACTIVE SYSTEMS: A FIELD STUDY A PREPRINT. Retrieved from http://www.philipbeesleyarchitect.com/sculptures/

View more

The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning

Meyer, B. J., & Drummond, T. (2019). The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning. Retrieved from https://arxiv.org/pdf/1902.10363

View more

Learning to Take Good Pictures of People with a Robot Photographer

Newbury, R., Cosgun, A., Koseoglu, M., & Drummond, T. (2019). Learning to Take Good Pictures of People with a Robot Photographer. Retrieved from https://arxiv.org/pdf/1904.05688

View more

SASSE: Scalable and Adaptable 6-DOF Pose Estimation

Le, H., Hoang, T., Zhang, Q., Do, T.-T., Eriksson, A., & Milford, M. (2019). SASSE: Scalable and Adaptable 6-DOF Pose Estimation. Retrieved from https://arxiv.org/pdf/1902.01549

View more

Visual SLAM: Why Bundle Adjust?

Parra Bustos, A., Chin, T.-J., Eriksson, A., & Reid, I. (2019). Visual SLAM: Why Bundle Adjust? Retrieved from https://arxiv.org/pdf/1902.03747

View more

RERERE: Remote Embodied Referring Expressions in Real indoor Environments

Qi, Y., Wu, Q., Anderson, P., Liu, M., Shen, C., & Van Den Hengel, A. (2019). RERERE: Remote Embodied Referring Expressions in Real indoor Environments. Retrieved from https://arxiv.org/pdf/1904.10151

View more

Constrained Design of Deep Iris Networks

Nguyen, K., Fookes, C., & Sridharan, S. (2019). Constrained Design of Deep Iris Networks. Retrieved from https://arxiv.org/pdf/1905.09481

View more

Learning to Adapt for Stereo

Tonioni, A., Rahnama, O., Joy, T., Di Stefano, L., Ajanthan, T., & Torr, P. H. S. (2019). Learning to Adapt for Stereo. Retrieved from http://arxiv.org/abs/1904.02957

View more

A Deep Journey into Super-resolution: A survey

Anwar, S., Khan, S., & Barnes, N. (2019). A Deep Journey into Super-resolution: A survey. Retrieved from http://arxiv.org/abs/1904.07523

View more

Super-Trajectories: A Compact Yet Rich Video Representation

Akhter, I., Fah, C. L., & Hartley, R. (2019). Super-Trajectories: A Compact Yet Rich Video Representation. Retrieved from http://arxiv.org/abs/1901.07273

View more

Bringing Blurry Alive at High Frame-Rate with an Event Camera

Pan, L., Hartley, R., Scheerlinck, C., Liu, M., Yu, X., & Dai, Y. (2019). Bringing Blurry Alive at High Frame-Rate with an Event Camera. Retrieved from http://arxiv.org/abs/1903.06531

View more

Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation

Kumar, S., Ghorakavi, R. S., Dai, Y., & Li, H. (2019). Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation. Retrieved from http://arxiv.org/abs/1902.03791

View more

Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry

Zhou, D., Dai, Y., & Li, H. (2019). Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry. Retrieved from http://arxiv.org/abs/1903.00912

View more

Continual Learning with Tiny Episodic Memories

Chaudhry, A., Rohrbach, M., Elhoseiny, M., Ajanthan, T., Dokania, P. K., Torr, P. H. S., & Ranzato, M. (2019). Continual Learning with Tiny Episodic Memories. Retrieved from http://arxiv.org/abs/1902.10486

View more

Neural Collaborative Subspace Clustering

Zhang, T., Ji, P., Harandi, M., Huang, W., & Li, H. (2019). Neural Collaborative Subspace Clustering. Retrieved from http://arxiv.org/abs/1904.10596

View more

Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images

Zhu, H., Guo, M., Li, H., Wang, Q., & Robles-Kelly, A. (2019). Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images. Retrieved from http://arxiv.org/abs/1902.05672

View more

Practical Robot Learning from Demonstrations using Deep End-to-End Training

Cosgun, A., Rowntree, T., Reid, I., & Drummond, T. (2019). Practical Robot Learning from Demonstrations using Deep End-to-End Training. Retrieved from https://arxiv.org/pdf/1905.09025

View more

Quickest Detection and Identification of Intermittent Signals with Application to Vision Based Aircraft Detection

James, J., Ford, J. J., & Molloy, T. L. (2019). Quickest Detection and Identification of Intermittent Signals with Application to Vision Based Aircraft Detection. Retrieved from http://arxiv.org/abs/1903.03270

View more

On the Informativeness of Measurements in Shiryaev’s Bayesian Quickest Change Detection

Ford, J. J., James, J., & Molloy, T. L. (2019). On the Informativeness of Measurements in Shiryaev’s Bayesian Quickest Change Detection. Retrieved from http://arxiv.org/abs/1903.03283

View more

Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid

James, J., Ford, J. J., & Molloy, T. L. (2019). Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid. Retrieved from http://arxiv.org/abs/1903.03275

View more

Geometric interpretation of the general POE model for a serial-link robot via conversion into D-H parameterization

Wu, L., Crawford, R., & Roberts, J. (2019). Geometric interpretation of the general POE model for a serial-link robot via conversion into D-H parameterization. Retrieved from http://arxiv.org/abs/1902.00198

View more

Optimal Dexterity for a Snake-like Surgical Manipulator using Patient-specific Task-space Constraints in a Computational Design Algorithm

Razjigaev, A., Pandey, A. K., Roberts, J., & Wu, L. (2019). Optimal Dexterity for a Snake-like Surgical Manipulator using Patient-specific Task-space Constraints in a Computational Design Algorithm. Retrieved from http://arxiv.org/abs/1903.02217

View more

SASSE: Scalable and Adaptable 6-DOF Pose Estimation

Le, H., Hoang, T., Zhang, Q., Do, T.-T., Eriksson, A., & Milford, M. (2019). SASSE: Scalable and Adaptable 6-DOF Pose Estimation. Retrieved from https://arxiv.org/abs/1902.01549

View more

LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles

Zeng, F., Jacobson, A., Smith, D., Boswell, N., Peynot, T., & Milford, M. (2019). LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles. Retrieved from https://arxiv.org/abs/1903.08313

View more

Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions

Zaffar, M., Khaliq, A., Ehsan, S., Milford, M., & McDonald-Maier, K. (2019). Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions. Retrieved from https://arxiv.org/abs/1903.09107

View more

Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?

Zaffar, M., Khaliq, A., Ehsan, S., Milford, M., Alexis, K., & McDonald-Maier, K. (2019). Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics? Retrieved from http://arxiv.org/abs/1904.07967

View more

Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

Rahman, Q. M., Sünderhauf, N., & Dayoub, F. (2019). Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors. Retrieved from http://arxiv.org/abs/1903.06391

View more

The Probabilistic Object Detection Challenge

Skinner, J., Hall, D., Zhang, H., Dayoub, F., & Sünderhauf, N. (2019). The Probabilistic Object Detection Challenge. Retrieved from http://arxiv.org/abs/1903.07840

View more

Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation

Garg, S., Babu, M., Dharmasiri, T., Hausler, S., Suenderhauf, N., Kumar, S., Drummond, T., & Milford, M. (2019). Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation. Retrieved from http://arxiv.org/abs/1902.07381

View more

Model-less Active Compliance for Continuum Robots using Recurrent Neural Networks

Jakes, D., Ge, Z., & Wu, L. (2019). Model-less Active Compliance for Continuum Robots using Recurrent Neural Networks. Retrieved from http://arxiv.org/abs/1902.08943

View more

An Adaptive Markov Random Field for Structured Compressive Sensing

Suwanwimolkul, S., Zhang, L., Gong, D., Zhang, Z., Chen, C., Ranasinghe, D. C., & Qinfeng Shi, J. (2019). An Adaptive Markov Random Field for Structured Compressive Sensing. IEEE Transactions on Image Processing, 28(3), 1556–1570. http://doi.org/10.1109/TIP.2018.2878294

View more

Australian Centre for Robotic Vision
2 George Street Brisbane, 4001
+61 7 3138 7549