Feras Dayoub

Dr Feras Dayoub

Senior Lecturer

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

I work at the intersection of computer vision, machine learning, and robotics, specializing in Embodied AI and Robotic Vision as part of the Australian Institute for Machine Learning (AIML) at the University of Adelaide. I co-direct CROSSING, a French-Australian laboratory focused on human-autonomous agent teaming. In addition to my role at AIML, I hold an Adjunct position at Queensland University of Technology (QUT) and serve as an Associate Investigator with the QUT Centre for Robotics. Previously, I was a Chief Investigator at the ARC Centre of Excellence for Robotic Vision. My research is dedicated to advancing the reliable deployment of computer vision and machine learning on mobile robots in real-world environments. I worked on applied robotic vision projects spanning agricultural innovation, environmental conservation, and autonomous infrastructure monitoring. As an educator, I am passionate about teaching programming, computer vision, machine learning, and robotic perception.

  • Journals
    Year Citation
    2023 Pershouse, D., Dayoub, F., Miller, D., & Sünderhauf, N. (2023). Addressing the Challenges of Open-World Object Detection.
    2023 Shi, X., Qiao, Y., Wu, Q., Liu, L., & Dayoub, F. (2023). Improving Online Source-free Domain Adaptation for Object Detection by
    Unsupervised Data Acquisition.
    2023 Chapman, N. H., Dayoub, F., Browne, W., & Lehnert, C. (2023). Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection. IEEE Robotics and Automation Letters, 8(8), 1-8.
    DOI Scopus1
    2022 Hall, D., Talbot, B., Bista, S. R., Zhang, H., Smith, R., Dayoub, F., & Sünderhauf, N. (2022). BenchBot environments for active robotics (BEAR): Simulated data for active scene understanding research. International Journal of Robotics Research, 41(3), 259-269.
    DOI Scopus2 WoS2
    2022 Rahman, Q. M., Sunderhauf, N., Corke, P., & Dayoub, F. (2022). FSNet: A Failure Detection Framework for Semantic Segmentation. IEEE Robotics and Automation Letters, 7(2), 1-8.
    DOI Scopus13 WoS5
    2022 Miller, D., Sunderhauf, N., Milford, M., & Dayoub, F. (2022). Uncertainty for identifying open-set errors in visual object detection. IEEE Robotics and Automation Letters, 7(1), 215-222.
    DOI Scopus33 WoS8
    2021 Talbot, B., Dayoub, F., Corke, P., & Wyeth, G. (2021). Robot navigation in unseen spaces using an abstract map. IEEE Transactions on Cognitive and Developmental Systems, 13(4), 791-805.
    DOI Scopus16 WoS7
    2021 Rahman, Q. M., Corke, P., & Dayoub, F. (2021). Run-time monitoring of machine learning for robotic perception: a survey of emerging trends. IEEE Access, 9, 20067-20075.
    DOI Scopus45 WoS16
    2020 Haviland, J., Dayoub, F., & Corke, P. (2020). Control of the Final-Phase of Closed-Loop Visual Grasping using
    Image-Based Visual Servoing.
    2020 Arain, B., Dayoub, F., Rigby, P., & Dunbabin, M. (2020). Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse
    Range Estimation.
    2019 Skinner, J., Hall, D., Zhang, H., Dayoub, F., & Sünderhauf, N. (2019). The Probabilistic Object Detection Challenge.
    2019 Sünderhauf, N., Dayoub, F., Hall, D., Skinner, J., Zhang, H., Carneiro, G., & Corke, P. (2019). A probabilistic challenge for object detection. Nature Machine Intelligence, 1(9), 443.
    DOI WoS1
    2018 Ahn, H. S., Sa, I., & Dayoub, F. (2018). Introduction to the Special Issue on Precision Agricultural Robotics and Autonomous Farming Technologies. IEEE Robotics and Automation Letters, 3(4), 4435-4438.
    DOI Scopus4 WoS2
    2018 Hall, D., Dayoub, F., Perez, T., & McCool, C. (2018). A rapidly deployable classification system using visual data for the application of precision weed management. Computers and Electronics in Agriculture, 148, 107-120.
    DOI Scopus21 WoS11
    2017 Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., Dayoub, F., . . . Perez, T. (2017). Robot for weed species plant-specific management. Journal of Field Robotics, 34(6), 1179-1199.
    DOI Scopus154 WoS75
    2017 Sa, I., Lehnert, C., English, A., McCool, C., Dayoub, F., Upcroft, B., & Perez, T. (2017). Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting-Combined Color and 3-D Information. IEEE Robotics and Automation Letters, 2(2), 765-772.
    DOI Scopus96 WoS59
    2016 Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). Deepfruits: A fruit detection system using deep neural networks. Sensors (Switzerland), 16(8), 1-23.
    DOI Scopus931 WoS539 Europe PMC143
    2015 Dayoub, F., Morris, T., & Corke, P. (2015). Rubbing shoulders with mobile service robots. IEEE Access, 3, 333-342.
    DOI Scopus8 WoS8
    2011 Dayoub, F., Cielniak, G., & Duckett, T. (2011). Long-term experiments with an adaptive spherical view representation for navigation in changing environments. Robotics and Autonomous Systems, 59(5), 285-295.
    DOI Scopus50 WoS37
    1999 Kleider, J. P., Longeaud, C., & Dayoub, F. (1999). Electronic properties of bottom gate silicon nitride/hydrogenated amorphous silicon structures. THIN SOLID FILMS, 337(1-2), 208-212.
    DOI
    1998 Kleider, J. P., & Dayoub, F. (1998). Theoretical and experimental study of the quasistatic capacitance of metal-insulator hydrogenated amorphous silicon structures: Strong evidence for the defect-pool model. PHYSICAL REVIEW B, 58(16), 10401-10414.
    DOI
    1997 Kleider, J. P., & Dayoub, F. (1997). Defect creation and removal in hydrogenated amorphous silicon predicted by the defect-pool model and revealed by the quasistatic capacitance of metal-insulator-semiconductor structures. PHYSICAL REVIEW B, 55(16), 10181-10184.
    DOI
    1994 FABRE, A., DAYOUB, F., DURUISSEAU, L., & KAMOUN, M. (1994). HIGH INPUT IMPEDANCE INSENSITIVE 2ND-ORDER FILTERS IMPLEMENTED FROM CURRENT CONVEYORS. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS, 41(12), 918-921.
    DOI
    1974 BRUNEL, A. L., BROCHERI, C., VANNIER, R., GRIFFIE, R. A., DAYOUB, F., & KANDIC, D. (1974). EXPERIMENTAL STUDIES ON CORTICOIDS IN CANAL TREATMENT. REVUE DE STOMATOLOGIE ET DE CHIRURGIE MAXILLO-FACIALE, 75(2), 302-306.
    - Clement, B., Dubromel, M., Santos, P. E., Sammut, K., Oppert, M., & Dayoub, F. (n.d.). Hybrid Navigation Acceptability and Safety. Proceedings of the AAAI Symposium Series, 2(1), 11-17.
    DOI

  • Books
    Year Citation
    2020 Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., . . . Milford, M. (2020). Semantics for Robotic Mapping, Perception and Interaction: A Survey (Vol. 8). United States: Now Publishers.
    DOI

  • Book Chapters
    Year Citation
    2017 Perez, T., Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., & Dayoub, F. (2017). Overview of mechatronic design for a weed-management robotic system. In D. Zhang, & B. Wei (Eds.), Robotics and Mechatronics for Agriculture (1st ed., pp. 23-49). Boca Raton, USA: CRC Press.
    DOI
    2015 Dayoub, F., Cielniak, G., & Duckett, T. (2015). Eight weeks of episodic visual navigation inside a non-stationary environment using adaptive spherical views. In L. Mejias, P. Corke, & J. Roberts (Eds.), Springer Tracts in Advanced Robotics (Vol. 105, pp. 379-392). SPRINGER-VERLAG BERLIN.
    DOI Scopus2 WoS2
    2011 Dayoub, F., Cielniak, G., & Duckett, T. (2011). Long-term experiment using an adaptive appearance-based map for visual navigation by mobile robots. In Lecture Notes in Computer Science (Vol. 6856 LNAI, pp. 400-401). Springer Berlin Heidelberg.
    DOI Scopus1

  • Conference Papers
    Year Citation
    2024 Wilson, S., Fischer, T., Dayoub, F., Miller, D., & Sünderhauf, N. (2024). SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023) (pp. 23508-23519). online: IEEE.
    DOI Scopus14
    2024 Wu, R., Wang, H., Dayoub, F., & Chen, H. T. (2024). Segment beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 6100-6108). Online: Association for the Advancement of Artificial Intelligence (AAAI).
    DOI Scopus1
    2024 Yuan, D., Maire, F., & Dayoub, F. (2024). Cross-Attention between Satellite and Ground Views for Enhanced Fine-Grained Robot Geo-Localization. In Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 (pp. 1238-1245). Online: IEEE.
    DOI Scopus3
    2024 Abou-Chakra, J., Dayoub, F., & Sunderhauf, N. (2024). ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields. In Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 (pp. 5963-5972). Online: IEEE.
    DOI Scopus4
    2024 Garg, S., Rana, K., Hosseinzadeh, M., Mares, L., Sünderhauf, N., Dayoub, F., & Reid, I. (2024). RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 35 (pp. 4090-4097). Yokohama, Japan: IEEE.
    DOI Scopus4
    2024 Yuan, D., Maire, F., & Dayoub, F. (2024). Temporal Attention for Cross-View Sequential Image Localization. In IEEE International Conference on Intelligent Robots and Systems (pp. 7429-7436). Abu Dhabi, United Arab Emirates: IEEE.
    DOI
    2024 Abou-Chakra, J., Rana, K., Dayoub, F., & Sünderhauf, N. (2024). Physically Embodied Gaussian Splatting: A Visually Learnt and Physically Grounded 3D Representation for Robotics. In Proceedings of Machine Learning Research Vol. 270 (pp. 513-530). Munich, Germany: ML Research Press.
    2024 McLeod, S., Chng, C. K., Ono, T., Shimizu, Y., Hemmi, R., Holden, L., . . . Chin, T. J. (2024). Robust Perspective-n-Crater for Crater-based Camera Pose Estimation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 6760-6769). Seattle: IEEE.
    DOI Scopus1
    2023 Wilson, S., Fischer, T., Sunderhauf, N., & Dayoub, F. (2023). Hyperdimensional Feature Fusion for Out-of-Distribution Detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023) (pp. 2643-2653). Online: IEEE.
    DOI Scopus10
    2022 Corke, P., Dayoub, F., Hall, D., Skinner, J., & Sünderhauf, N. (2022). What Can Robotics Research Learn from Computer Vision Research?. In Proceedings of the 19th International Symposium of Robotic Research (ISRR 2019), as published in Springer Proceedings in Advanced Robotics Vol. 20 (pp. 987-1003). Cham, Switzerland: Springer.
    DOI
    2021 Moskvyak, O., Maire, F., Dayoub, F., Armstrong, A. O., & Baktashmotlagh, M. (2021). Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings. In DICTA 2021 - 2021 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). online: IEEE.
    DOI Scopus20 WoS1
    2021 Bista, S. R., Hall, D., Talbot, B., Zhang, H., Dayoub, F., & Sünderhauf, N. (2021). Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5328-5335). online: IEEE.
    DOI Scopus10 WoS6
    2021 Miller, D., Sunderhauf, N., Milford, M., & Dayoub, F. (2021). Class anchor clustering: A loss for distance-based open set recognition. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2021) (pp. 3569-3577). online: IEEE.
    DOI Scopus106 WoS31
    2021 Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2021). Keypoint-aligned embeddings for image retrieval and re-identification. In Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 (pp. 676-685). online: IEEE.
    DOI Scopus24 WoS9
    2021 Zhang, H., Wang, Y., Dayoub, F., & Sünderhauf, N. (2021). VarifocalNet: An IoU-aware Dense Object Detector. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 8510-8519). online: IEEE.
    DOI Scopus760 WoS212
    2021 Rahman, Q. M., Sunderhauf, N., & Dayoub, F. (2021). Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection during Deployment. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW 2021) (pp. 152-160). online: IEEE.
    DOI Scopus16 WoS7
    2021 Rahman, Q. M., Sünderhauf, N., & Dayoub, F. (2021). Online Monitoring of Object Detection Performance During Deployment. In Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) (pp. 4839-4845). online: IEEE.
    DOI Scopus9 WoS3
    2021 Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2021). SEMI-SUPERVISED KEYPOINT LOCALIZATION. In ICLR 2021 - 9th International Conference on Learning Representations (pp. 1-11). Virtual only: Open Review.
    Scopus14
    2020 Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2020). Learning Landmark Guided Embeddings for Animal Re-identification. In 2020 IEEE Winter Applications of Computer Vision Workshops (WACVW) (pp. 12-19). online: IEEE.
    DOI Scopus9 WoS8
    2020 Hall, D., Dayoub, F., Skinner, J., Zhang, H., Miller, D., Corke, P., . . . Sunderhauf, N. (2020). Probabilistic object detection: Definition and evaluation. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 1020-1029). online: IEEE.
    DOI Scopus81 WoS38
    2019 Halodova, L., Dvorrakova, E., Majer, F., Vintr, T., Mozos, O. M., Dayoub, F., & Krajnik, T. (2019). Predictive and adaptive maps for long-term visual navigation in changing environments. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7033-7039). Macau, China: IEEE.
    DOI Scopus20 WoS13
    2019 Miller, D., Dayoub, F., Milford, M., & Sunderhauf, N. (2019). Evaluating merging strategies for sampling-based uncertainty techniques in object detection. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2019) (pp. 2348-2354). online: IEEE.
    DOI Scopus87 WoS38
    2019 Rahman, Q. M., Sunderhauf, N., & Dayoub, F. (2019). Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3748-3753). online: IEEE.
    DOI Scopus21 WoS12
    2019 Miller, D., Sünderhauf, N., Zhang, H., Hall, D., & Dayoub, F. (2019). Benchmarking sampling-based probabilistic object detectors. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2019-June (pp. 42-45).
    Scopus11
    2018 Abbas, A., Maire, F., Shirazi, S., Dayoub, F., & Eich, M. (2018). A dynamic planner for object assembly tasks based on learning the spatial relationships of its parts from a single demonstration. In T. Mitrovic, B. Xue, & X. Li (Eds.), Proceedings of AI 2018: Advanced in Artificial Intelligence 31st Australasian Joint Conference Vol. 11320 LNAI (pp. 759-765). Wellington, New Zealand: Springer International Publishing.
    DOI Scopus2
    2018 McFadyen, A., Dayoub, F., Martin, S., Ford, J., & Corke, P. (2018). Assisted Control for Semi-Autonomous Power Infrastructure Inspection Using Aerial Vehicles. In IEEE International Conference on Intelligent Robots and Systems (pp. 5719-5726). online: IEEE.
    DOI Scopus3 WoS1
    2018 Miller, D., Nicholson, L., Dayoub, F., & Sunderhauf, N. (2018). Dropout Sampling for Robust Object Detection in Open-Set Conditions. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2018) (pp. 3243-3249). Brisbane, Australia: IEEE.
    DOI Scopus190 WoS75
    2018 Abbas, A., Maire, F., Dayoub, F., & Shirazi, S. (2018). Combining learning from demonstration and search algorithm for dynamic goal-directed assembly task planning. In ACRA 2018 Proceedings Vol. 2018-December. Online: Australian Robotics and Automation Association.
    2017 Dayoub, F., Sunderhauf, N., & Corke, P. I. (2017). Episode-Based Active Learning with Bayesian Neural Networks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2017) Vol. 2017-July (pp. 498-500). Honolulu, Hawaii, USA: IEEE.
    DOI Scopus7 WoS3
    2017 Hall, D., Dayoub, F., Kulk, J., & McCool, C. (2017). Towards unsupervised weed scouting for agricultural robotics. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 5223-5230). online: IEEE.
    DOI Scopus33
    2017 Hall, D., Dayoub, F., Perez, T., & McCool, C. (2017). A transplantable system for weed classification by agricultural robotics. In 2017 IEEE International Conference on Intelligent Robots and Systems Vol. 2017-September (pp. 5174-5179). Vancouver, BC, Canada: IEEE.
    DOI Scopus5 WoS4
    2016 Sunderhauf, N., Dayoub, F., McMahon, S., Talbot, B., Schulz, R., Corke, P., . . . Milford, M. (2016). Place categorization and semantic mapping on a mobile robot. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2016-June (pp. 5729-5736). online: IEEE.
    DOI Scopus117 WoS75
    2016 McCool, C., Sa, I., Dayoub, F., Lehnert, C., Perez, T., & Upcroft, B. (2016). Visual detection of occluded crop: For automated harvesting. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2016-June (pp. 2506-2512). Stockholm, Sweden: IEEE.
    DOI Scopus65 WoS38
    2016 Talbot, B., Lam, O., Schulz, R., Dayoub, F., Upcroft, B., & Wyeth, G. (2016). Find my office: Navigating real space from semantic descriptions. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2016-June (pp. 5782-5787). online: IEEE.
    DOI Scopus19 WoS12
    2015 Schulz, R., Talbot, B., Lam, O., Dayoub, F., Corke, P., Upcroft, B., & Wyeth, G. (2015). Robot navigation using human cues: A robot navigation system for symbolic goal-directed exploration. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2015-June (pp. 1100-1105). Seattle, WA: IEEE COMPUTER SOC.
    DOI Scopus28 WoS12
    2015 Dayoub, F., Dunbabin, M., & Corke, P. (2015). Robotic detection and tracking of Crown-of-Thorns starfish. In IEEE International Conference on Intelligent Robots and Systems Vol. 2015-December (pp. 1921-1928). Hamburg, GERMANY: IEEE.
    DOI Scopus30 WoS19
    2015 Sünderhauf, N., Shirazi, S., Jacobson, A., Dayoub, F., Pepperell, E., Upcroft, B., & Milford, M. (2015). Place recognition with convnet landmarks: Viewpoint-robust, condition-robust, training-free. In Proceedings of the Robotics: Science and Systems XI Conference (RSS 2015) Vol. 11 (pp. 1-10). online: Robotics: Science and Systems Foundation.
    DOI Scopus326 WoS191
    2015 Hall, D., McCool, C., Dayoub, F., Sünderhauf, N., & Upcroft, B. (2015). Evaluation of features for leaf classification in challenging conditions. In Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015 (pp. 797-804). HI, Waikoloa: IEEE.
    DOI Scopus109 WoS69
    2015 Sünderhauf, N., Shirazi, S., Dayoub, F., Upcroft, B., & Milford, M. (2015). On the performance of ConvNet features for place recognition. In IEEE International Conference on Intelligent Robots and Systems Vol. 2015-December (pp. 4297-4304). Hamburg, GERMANY: IEEE.
    DOI Scopus462 WoS327
    2015 Lam, O., Dayoub, F., Schulz, R., & Corke, P. (2015). Automated topometric graph generation from floor plan analysis. In Australasian Conference on Robotics and Automation, ACRA.
    Scopus9
    2014 Lam, O., Dayoub, F., Schulz, R., & Corke, P. (2014). Text recognition approaches for indoor robotics: A comparison. In Australasian Conference on Robotics and Automation, ACRA Vol. 02-04-December-2014.
    Scopus4
    2014 Morris, T., Dayoub, F., Corke, P., & Upcroft, B. (2014). Simultaneous localization and planning on multiple map hypotheses. In IEEE International Conference on Intelligent Robots and Systems (pp. 4531-4536). Chicago, IL: IEEE.
    DOI Scopus5 WoS3
    2014 Morris, T., Dayoub, F., Corke, P., Wyeth, G., & Upcroft, B. (2014). Multiple map hypotheses for planning and navigating in non-stationary environments. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 2765-2770). Hong Kong, PEOPLES R CHINA: IEEE.
    DOI Scopus19 WoS12
    2013 Dayoub, F., Morris, T., Upcroft, B., & Corke, P. (2013). Vision-only autonomous navigation using topometric maps. In N. Amato (Ed.), IEEE International Conference on Intelligent Robots and Systems (pp. 1923-1929). JAPAN, Tokyo: IEEE.
    DOI Scopus34 WoS21
    2013 Dayoub, F., Morris, T., Upcroft, B., & Corke, P. (2013). One Robot, eight hours, and twenty four thousand people. In Australasian Conference on Robotics and Automation, ACRA.
    Scopus1
    2010 Dayoub, F., Duckett, T., & Cielniak, G. (2010). Short- and long-term adaptation of visual place memories for mobile robots. In Proceedings of the International Symposium on Remembering Who We Are - Human Memory for Artificial Agents - A Symposium at the AISB 2010 Convention (pp. 21-26).
    Scopus2
    2008 Dayoub, F., & Duckett, T. (2008). An adaptive appearance-based map for long-term topological localization of mobile robots. In R. Chatila, A. Kelly, & J. P. Merlet (Eds.), 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (pp. 3364-3369). Nice, FRANCE: IEEE.
    DOI Scopus78 WoS48
    1997 Dayoub, F., Kleider, J. P., & Mencaraglia, D. (1997). Comparison of Al/SiN/a-Si:H and Al/SiO2/a-Si:H top gate structures under thermal bias stresses. In THIN SOLID FILMS Vol. 296 (pp. 137-140). FRANCE, STRASBOURG: ELSEVIER SCIENCE SA LAUSANNE.
    DOI
    1996 Dayoub, F., Kleider, J. P., Longeaud, C., Mencaraglia, D., & Reynaud, J. (1996). Thermal bias annealing experiments on aluminum silicon nitride hydrogenated amorphous silicon top gate structures. In JOURNAL OF NON-CRYSTALLINE SOLIDS Vol. 200 (pp. 318-321). JAPAN, KOBE: ELSEVIER SCIENCE BV.

  • Preprint
    Year Citation
    2024 Mallick, P., Dayoub, F., & Sherrah, J. (2024). Wasserstein Distance-based Expansion of Low-Density Latent Regions for
    Unknown Class Detection.
    2024 Yuan, D., Maire, F., & Dayoub, F. (2024). Temporal Attention for Cross-View Sequential Image Localization.
    2024 Lin, C. -J., Garg, S., Chin, T. -J., & Dayoub, F. (2024). Robust Scene Change Detection Using Visual Foundation Models and
    Cross-Attention Mechanisms.
    2024 Bockman, J., Howe, M., Orenstein, A., & Dayoub, F. (2024). AARK: An Open Toolkit for Autonomous Racing Research.
    2024 Garg, S., Rana, K., Hosseinzadeh, M., Mares, L., Sünderhauf, N., Dayoub, F., & Reid, I. (2024). RoboHop: Segment-based Topological Map Representation for Open-World
    Visual Navigation.
    2024 Abraham, S. S., Garg, S., & Dayoub, F. (2024). To Ask or Not to Ask? Detecting Absence of Information in Vision and
    Language Navigation.
    2024 Abou-Chakra, J., Rana, K., Dayoub, F., & Sünderhauf, N. (2024). Physically Embodied Gaussian Splatting: A Realtime Correctable World
    Model for Robotics.
    2023 Holden, L., Dayoub, F., Harvey, D., & Chin, T. -J. (2023). Federated Neural Radiance Fields.
    2023 Wu, R., Wang, H., Dayoub, F., & Chen, H. -T. (2023). Segment Beyond View: Handling Partially Missing Modality for
    Audio-Visual Semantic Segmentation.
    2022 Wilson, S., Fischer, T., Dayoub, F., Miller, D., & Sünderhauf, N. (2022). SAFE: Sensitivity-Aware Features for Out-of-Distribution Object
    Detection.

  • Current Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2025 Co-Supervisor Exploring Human-Robot Interaction as a Collaborative Model for One-to-Many Teaming in Aerospace Environments Doctor of Philosophy Doctorate Full Time Mrs Donna Lee Duffy
    2024 Principal Supervisor Foundation models for goal-oriented reinforcement learning and intrinsic exploration Master of Philosophy Master Full Time Mr Dustin Wyly Craggs
    2024 Principal Supervisor Robust Machine Learning Techniques for RF Signal Classification on Sparse and Noisy Digitally Sampled Radar Data Master of Philosophy Master Full Time Mr Sebastian Luke McCormack Cocks
    2024 Co-Supervisor Hand-held Object Identification, Segmentation, and Tracking in the Wild Doctor of Philosophy Doctorate Full Time Mr Huy Anh Nguyen
    2024 Principal Supervisor Data-driven physically plausible dexterous manipulation Doctor of Philosophy Doctorate Full Time Mr King Hang Wong
    2023 Principal Supervisor 3D Scene Understanding and Change Tracking Doctor of Philosophy Doctorate Full Time Mr Chun-Jung Lin
    2023 Co-Supervisor 3D indoor Scene Reconstruction Doctor of Philosophy Doctorate Full Time Mr Wenbo Zhang
    2023 Principal Supervisor Semi-supervised object detection for mobile robots Master of Philosophy Master Full Time Mr Xiangyu Shi
    2022 Co-Supervisor Enhancing Mars rovers using AI-enabled robotic vision Doctor of Philosophy Doctorate Full Time Mr Lachlan William Holden

  • Past Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2022 - 2024 Co-Supervisor Towards Pedestrian Safety Augmented Reality System Master of Philosophy Master Full Time Mr Renjie Wu
  • Position: Senior Lecturer
  • Email: feras.dayoub@adelaide.edu.au
  • Campus: North Terrace
  • Org Unit: Australian Institute for Machine Learning - Projects
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