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2019 Conference Papers [61]

Real-time joint semantic segmentation and depth estimation using asymmetric annotations

Nekrasov, V., Dharmasiri, T., Spek, A., Drummond, T., Shen, C., & Reid, I. (2019). Real-time joint semantic segmentation and depth estimation using asymmetric annotations. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 7101–7107. https://doi.org/10.1109/ICRA.2019.8794220

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Social Robots with Gamification Principles to Increase Long-Term User Interaction

Robinson, N. L., Turkay, S., Cooper, L. A. N., & Johnson, D. (2019, December 2). Social Robots with Gamification Principles to Increase Long-Term User Interaction. 359–363. https://doi.org/10.1145/3369457.3369494

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Evaluation of Vision-based Surface Crack Detection Methods for Underground Mine Tunnel Images

Azhari, F., Sennersten, C., & Peynot, T. (2019). Evaluation of vision-based surface crack detection methods for underground mine tunnel images. Proceedings of Australasian Conference on Robotics and Automation 2019.

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Control Comparison and Evaluation of Pneumatic & Electric Linear Actuators for Configurable Center-Hub Wheels

Pond, L., Hojnik, T., Flick, P., & Roberts, J. (2019). Control comparison and evaluation of pneumatic & electric linear actuators for configurable center-hub wheels. ACRA 2019 Proceedings.

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Star Tracking using an Event Camera

Chin, T.-J., Bagchi, S., Eriksson, A., & Van Schaik, A. (n.d.). Star Tracking using an Event Camera.

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Bushfire emergency response simulation

Bruggemann, T. S., Ford, J. J., White, G., & Perez, T. (2019). Bushfire emergency response simulation.

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Bushfire emergency response uncertainty quantification

Bruggemann, T., Ford, J. J., White, G., Perez, T., & Power, W. (2019). Bushfire emergency response uncertainty quantification. Proceedings of the 23rd International Congress on Modelling and Simulation (MODSIM2019).

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Deep Hashing by Discriminating Hard Examples

Yan, C., Pang, G., Bai, X., Shen, C., Zhou, J., Hancock, E., & Yang, C. (2019). Deep Hashing by Discriminating Hard Examples. https://doi.org/10.1145/3343031.3350927

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New Convex Relaxations for MRF Inference with Unknown Graphs

Wang, Z., Liu, T., Shi, Q., Pawan Kumar, M., & Zhang, J. (2019). New Convex Relaxations for MRF Inference with Unknown Graphs.

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Learning to Find Common Objects Across Few Image Collections

Shaban, A., Rahimi, A., Bansal, S., Gould, S., Boots, B., & Hartley, R. (2019). Learning to Find Common Objects Across Few Image Collections. Retrieved from http://arxiv.org/abs/1904.12936

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Bilinear Attention Networks for Person Retrieval

Fang, P., Zhou, J., Kumar Roy, S., Petersson, L., & Harandi, M. (2019). Bilinear Attention Networks for Person Retrieval.

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Siamese Networks: The Tale of Two Manifolds

Kumar Roy, S., Harandi, M., Nock, R., & Hartley, R. (n.d.). Siamese Networks: The Tale of Two Manifolds. Retrieved from https://github.com/sumo8291/

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Predicting the Future: A Jointly Learnt Model for Action Anticipation

Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2019). Predicting the Future: A Jointly Learnt Model for Action Anticipation.

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Hierarchical Encoding of Sequential Data With Compact and Sub-Linear Storage Cost

Le, H., Xu, M., Hoang, T., & Milford, M. (2019). Hierarchical Encoding of Sequential Data with Compact and Sub-linear Storage Cost.

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Watch, Reason and Code: Learning to Represent Videos Using Program

Duan, X., Wu, Q., Gan, C., Zhang, Y., Huang, W., van den Hengel, A., & Zhu, W. (2019). Watch, Reason and Code. In Proceedings of the 27th ACM International Conference on Multimedia - MM ’19 (pp. 1543–1551). New York, New York, USA: ACM Press. https://doi.org/10.1145/3343031.3351094

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Camera Relocalization by Exploiting Multi-View Constraints for Scene Coordinates Regression

Cai, M., Zhan, H., Saroj Weerasekera, C., Li, K., & Reid, I. (2019). Camera Relocalization by Exploiting Multi-View Constraints for Scene Coordinates Regression. http://openaccess.thecvf.com/content_ICCVW_2019/papers/DL4VSLAM/Cai_Camera_Relocalization_by_Exploiting_Multi-View_Constraints_for_Scene_Coordinates_Regression_ICCVW_2019_paper

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Silhouette-Assisted 3D Object Instance Reconstruction from a Cluttered Scene

Li, L., Khan, S., & Barnes ANU, N. (2019). Silhouette-Assisted 3D Object Instance Reconstruction from a Cluttered Scene.

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Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation

Pan, L., Dai, Y., Liu, M., Porikli, F., & Pan, Q. (2019). Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation. Retrieved from http://arxiv.org/abs/1910.02442

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Unsupervised Extraction of Local Image Descriptors via Relative Distance Ranking Loss

Yu, X., Tian, Y., Porikli, F., Hartley, R., Li, H., Heijnen, H., & Balntas, V. (2019). Unsupervised Extraction of Local Image Descriptors via Relative Distance Ranking Loss.

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Unsupervised Primitive Discovery for Improved 3D Generative Modeling

Khan, S. H., Guo, Y., Hayat, M., & Barnes, N. (2019). Unsupervised Primitive Discovery for Improved 3D Generative Modeling. Retrieved from https://salman-h-khan.github.io/papers/CVPR19_2

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Deep Segmentation-Emendation Model for Gland Instance Segmentation

Xie, Y., Lu, H., Zhang, J., Shen, C., & Xia, Y. (2019). Deep Segmentation-Emendation Model for Gland Instance Segmentation. https://doi.org/10.1007/978-3-030-32239-7_52

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A Generative Adversarial Density Estimator

Abbasnejad, M. E., Shi, J., Van Den Hengel, A., & Liu, L. (n.d.). A Generative Adversarial Density Estimator. _2019/papers/Abbasnejad_A_Generative_Adversarial_Density_Estimator_CVPR_2019_paper.pdf

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Adversarial Pulmonary Pathology Translation for Pairwise Chest X-Ray Data Augmentation

Xing, Y., Ge, Z., Zeng, R., Mahapatra, D., Seah, J., Law, M., & Drummond, T. (2019). Adversarial Pulmonary Pathology Translation for Pairwise Chest X-Ray Data Augmentation. https://doi.org/10.1007/978-3-030-32226-7_84

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Parallel Optimal Transport GAN

Avraham, G., Zuo, Y., & Drummond, T. (2019). Parallel Optimal Transport GAN *. Retrieved from http://openaccess.thecvf.com/content_CVPR_2019/html/Avraham_Parallel_Optimal_Transport_GAN_CVPR_2019_paper.html

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Neighbourhood context embeddings in deep inverse reinforcement learning for predicting pedestrian motion over long time horizons

Felix, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019). Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space. Retrieved from http://arxiv.org/abs/1908.04930

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Target-Aware Deep Tracking

Li, X., Ma, C., Wu, B., He, Z., & Yang, M.-H. (2019). Target-Aware Deep Tracking. Retrieved from http://arxiv.org/abs/1904.01772

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Residual Multiscale Based Single Image Deraining

Zheng, Y., Yu, X., Liu, M., & Zhang, S. (2019). YP.ZHENG ET AL: RESIDUAL MULTISCALE BASED SINGLE IMAGE DERAINING Residual Multiscale Based Single Image Deraining.

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Mind your neighbours: Image annotation with metadata neighbourhood graph co-attention networks

Zhang, J., Wu, Q., Zhang, J., Shen, C., & Lu, J. (2019). Mind Your Neighbours: Image Annotation with Metadata Neighbourhood Graph Co-Attention Networks. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2956-2964

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Airborne Particle Classification in LiDAR Point Clouds Using Deep Learning

Stanislas, L., Nubert, J., Dugas, D., Nitsch, J., Sünderhauf, N., Siegwart, R., Cadena, C., Peynot, T. (2019). Airborne Particle Classification in LiDAR Point Clouds Using Deep Learning * indicates equal contributions. Retrieved from https://leo-stan.github.io/particles_detection_fsr

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Group Surfing: A Pedestrian-Based Approach to Sidewalk Robot Navigation

Du, Y., Hetherington, N. J., Oon, C. L., Chan, W. P., Quintero, C. P., Croft, E., & Machiel Van der Loos, H. F. (2019). Group Surfing: A Pedestrian-Based Approach to Sidewalk Robot Navigation (pp. 6518–6524). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/icra.2019.8793608

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A study of X-vector based speaker recognition on short utterances

Kanagasundaram, A., Sridharan, S., Ganapathy, S., Singh, P., & Fookes, C. B. (2019). A study of X-vector based speaker recognition on short utterances.

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Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization

Shi,Y., Liu, L., Yu, X., Li, H., Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization, accepted at NEUIPS 2019

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Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse

Zhang, K., Luo, W., Ma, L., & Li, H. (2019). Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse. In AAAI 2019 (pp. 9203–9210). Retrieved from www.aaai.org

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Rotation Averaging with the Chordal Distance: Global Minimizers and Strong Duality

Eriksson, A., Olsson, C., Kahl, F., & Chin, T.-J. (2019). Rotation Averaging with the Chordal Distance: Global Minimizers and Strong Duality. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2019.2930051

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An Evaluation of Feature Matchers for Fundamental Matrix Estimation

Bian, J.-W., Wu, Y.-H., Zhao, J., Liu, Y., Zhang, L., Cheng, M.-M., & Reid, I. (n.d.). An Evaluation of Feature Matchers for Fundamental Matrix Estimation. Retrieved from https://jwbian.net/Papers/FM_BMVC19.pdf

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Deep Anomaly Detection with Deviation Networks

Pang, G., Shen, C., & Van Den Hengel, A. (n.d.). Deep Anomaly Detection with Deviation Networks. https://doi.org/10.1145/3292500.3330871

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Model-free Tracker for Multiple Objects Using Joint Appearance and Motion Inference

Liu, C., Yao, R., Rezatofighi, S. H., Reid, I., & Shi, Q. (2019). Model-free tracker for multiple objects using joint appearance and motion inference. IEEE Transactions on Image Processing, 1–1. https://doi.org/10.1109/TIP.2019.2928123

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EMPNet: Neural Localisation and Mapping Using Embedded Memory Points

Avraham, G., Zuo, Y., Dharmasiri, T., & Drummond, T. (2019). EMPNet: Neural Localisation and Mapping Using Embedded Memory Points Previous Camera movement Real World Trajectory Embedded Memory Points Embedded Memory Points Network Dense Embedding Correspondences Point-Embeddings CNN Alignment Current Observation Pr.

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Visual Controllers for Relative Positioning in Indoor Settings

Mejias, L., & Campoy, P. (2019). Visual controllers for relative positioning in indoor settings. Retrieved from https://eprints.qut.edu.au/131165/

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Real-time Vision-only Perception for Robotic Coral Reef Monitoring and Management

Dunbabin, M., Dayoub, F., Lamont, R., & Martin, S. (2019). Real-time Vision-only Perception for Robotic Coral Reef Monitoring and Management. Retrieved from http://icra-2019-uwroboticsperception.ge.issia.cnr.it/assets/ICRA19-WS-URP-CameraReadySubmissions/ICRA19-WS-URP-Paper-20

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Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes

Wang, Y., Shi, Q., Abbasnejad, E., Ma, C., Ma, X., & Zeng, B. (2019). Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes. Retrieved from https://arxiv.org/pdf/1906.09433

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CVPR19 Tracking and Detection Challenge: How crowded can it get?

Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., … Taixé, T. (n.d.). CVPR19 Tracking and Detection Challenge: How crowded can it get? Retrieved from http://www.motchallenge.net/

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Dynamic Manipulation of Gear Ratio and Ride Height for a Novel Compliant Wheel using Pneumatic Actuators

Hojnik, T., Flick, P., Bandyopadhyay, T., & Roberts, J. (n.d.). Dynamic Manipulation of Gear Ratio and Ride Height for a Novel Compliant Wheel using Pneumatic Actuators. Retrieved from https://eprints.qut.edu.au/130717/

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Benchmarking Sampling-based Probabilistic Object Detectors

Miller, D., Sünderhauf, N., Zhang, H., Hall, D., & Dayoub, F. (n.d.). Benchmarking Sampling-based Probabilistic Object Detectors. Retrieved from http://openaccess.thecvf.com/content_CVPRW_2019/papers/Uncertainty and Robustness in Deep Visual Learning/Miller_Benchmarking_Sampling-based_Probabilistic_Object_Detectors_CVPRW_2019_paper.pdf

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Event Cameras, Contrast Maximization and Reward Functions: An Analysis

Stoffregen, T., & Kleeman, L. (2019). Event Cameras, Contrast Maximization and Reward Functions: an Analysis.

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Binary Constrained Deep Hashing Network for Image Retrieval Without Manual Annotation

"T. Do et al., ""Binary Constrained Deep Hashing Network for Image Retrieval Without Manual Annotation,"" 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, 2019, pp. 695-704. doi: 10.1109/WACV.2019.00079"

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Multi-Scale Dense Networks for Deep High Dynamic Range Imaging

"Q. Yan et al., ""Multi-Scale Dense Networks for Deep High Dynamic Range Imaging,"" 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, 2019, pp. 41-50. doi: 10.1109/WACV.2019.00012"

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TopNet: Structural Point Cloud Decoder

Tchapmi, L. P., Kosaraju, V., Rezatofighi, H., Reid, I., & Savarese, S. (2019). TopNet: Structural Point Cloud Decoder. Retrieved from http://openaccess.thecvf.com/content_CVPR_2019/html/Tchapmi_TopNet_Structural_Point_Cloud_Decoder_CVPR_2019_paper.html

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A Generative Adversarial Density Estimator

Ehsan Abbasnejad, M., Shi, Q., van den Hengel, A., & Liu, L. (2019). A Generative Adversarial Density Estimator. Retrieved from http://openaccess.thecvf.com/content_CVPR_2019/html/Abbasnejad_A_Generative_Adversarial_Density_Estimator_CVPR_2019_paper.html

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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 https://youtu.be/0q6ap

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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

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Min-Max Statistical Alignment for Transfer Learning

Herath, S., Harandi, M., Fernando, B., Nock, R., & Basura, F. (2019). Min-Max Statistical Alignment for Transfer Learning.

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Expression of Curiosity in Social Robots

Ceha, J., Chhibber, N., Goh, J., McDonald, C., Oudeyer, P.-Y., Kulić, D., & Law, E. (2019). Expression of Curiosity in Social Robots. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19, 1–12. https://doi.org/10.1145/3290605.3300636

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Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions

Wilde, N., Kulic, D., & Smith, S. L. (2019). Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions. IEEE Robotics and Automation Letters, 4(2), 1691–1698. https://doi.org/10.1109/LRA.2019.2897342

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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. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 2924–2931. https://doi.org/10.1109/ICRA.2019.8794188

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Homography estimation of a moving planar scene from direct point correspondence

De Marco, S., Hua, M. D., Mahony, R., & Hamel, T. (2019). Homography estimation of a moving planar scene from direct point correspondence. In Proceedings of the IEEE Conference on Decision and Control (Vol. 2018–Decem, pp. 565–570). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CDC.2018.8619386

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Single image deblurring and camera motion estimation with depth map

Pan, L., Dai, Y., & Liu, M. (2019). Single image deblurring and camera motion estimation with depth map. In Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019 (pp. 2116–2125). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/WACV.2019.00229

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On the structure of kinematic systems with complete symmetry

Trumpf, J., Mahony, R., & Hamel, T. (2019). On the structure of kinematic systems with complete symmetry. In Proceedings of the IEEE Conference on Decision and Control (Vol. 2018–Decem, pp. 1276–1280). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CDC.2018.8619718

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Deep Learning AI for Corrosion Detection

Nash, W., Drummond, T., & Birbilis, N. (2019, May 15). Deep Learning AI for Corrosion Detection. Retrieved from https://www.onepetro.org/conference-paper/NACE-2019-13267

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

Garg, S., Babu, M. V., 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. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 4916–4923. https://doi.org/10.1109/ICRA.2019.8794178

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Recovering Faces From Portraits with Auxiliary Facial Attributes

*Shiri, F., Yu, X., Porikli, F., Hartley, R., & Koniusz, P. (2019). Recovering Faces From Portraits with Auxiliary Facial Attributes. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 406–415). Waikoloa Village, Hawaii, United States: IEEE. http://doi.org/10.1109/WACV.2019.00049

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