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2020 – today
- 2024
- [c44]Renjie Wu, Hu Wang, Feras Dayoub, Hsiang-Ting Chen:
Segment beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation. AAAI 2024: 6100-6108 - [c43]Sofia McLeod, Chee Kheng Chng, Tatsuharu Ono, Yuta Shimizu, Ryodo Hemmi, Lachlan Holden, Matthew Rodda, Feras Dayoub, Hirdy Miyamoto, Yukihiro Takahashi, Yasuko Kasai, Tat-Jun Chin:
Robust Perspective-n-Crater for Crater-based Camera Pose Estimation. CVPR Workshops 2024: 6760-6769 - [c42]Sourav Garg, Krishan Rana
, Mehdi Hosseinzadeh, Lachlan Mares, Niko Sünderhauf
RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation. ICRA 2024: 4090-4097 - [c41]Dong Yuan, Frédéric Maire, Feras Dayoub:
Cross-Attention Between Satellite and Ground Views for Enhanced Fine-Grained Robot Geo-Localization. WACV 2024: 1238-1245 - [c40]Jad Abou-Chakra, Feras Dayoub, Niko Sünderhauf
ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields. WACV 2024: 5963-5972 - [i53]Prakash Mallick, Feras Dayoub, Jamie Sherrah:
Wasserstein Distance-based Expansion of Low-Density Latent Regions for Unknown Class Detection. CoRR abs/2401.05594 (2024) - [i52]Nicolas Harvey Chapman, Feras Dayoub, Will N. Browne, Chris Lehnert:
Enhancing Embodied Object Detection through Language-Image Pre-training and Implicit Object Memory. CoRR abs/2402.03721 (2024) - [i51]Jiajun Deng, Sha Zhang, Feras Dayoub, Wanli Ouyang, Yanyong Zhang, Ian Reid:
PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interest. CoRR abs/2403.09212 (2024) - [i50]Benoit Clement, Marie Dubromel, Paulo E. Santos, Karl Sammut, Michelle Oppert, Feras Dayoub:
Hybrid Navigation Acceptability and Safety. CoRR abs/2404.11882 (2024) - [i49]Sourav Garg, Krishan Rana, Mehdi Hosseinzadeh, Lachlan Mares
RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation. CoRR abs/2405.05792 (2024) - [i48]Jad Abou-Chakra, Krishan Rana, Feras Dayoub, Niko Sünderhauf:
Physically Embodied Gaussian Splatting: A Realtime Correctable World Model for Robotics. CoRR abs/2406.10788 (2024) - [i47]Dong Yuan, Frédéric Maire, Feras Dayoub:
Temporal Attention for Cross-View Sequential Image Localization. CoRR abs/2408.15569 (2024) - [i46]Chun-Jung Lin, Sourav Garg, Tat-Jun Chin, Feras Dayoub:
Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms. CoRR abs/2409.16850 (2024) - [i45]James Bockman, Matthew Howe, Adrian Orenstein, Feras Dayoub:
AARK: An Open Toolkit for Autonomous Racing Research. CoRR abs/2410.00358 (2024) - [i44]Wenbo Zhang, Yang Li, Yanyuan Qiao, Siyuan Huang, Jiajun Liu, Feras Dayoub, Xiao Ma, Lingqiao Liu:
Effective Tuning Strategies for Generalist Robot Manipulation Policies. CoRR abs/2410.01220 (2024) - 2023
- [j15]Nicolas Harvey Chapman
Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection. IEEE Robotics Autom. Lett. 8(8): 5084-5091 (2023) - [c39]Samuel Wilson, Tobias Fischer
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection. ICCV 2023: 23508-23519 - [c38]Samuel Wilson, Tobias Fischer
Hyperdimensional Feature Fusion for Out-of-Distribution Detection. WACV 2023: 2643-2653 - [i43]Nicolas Harvey Chapman, Feras Dayoub, Will N. Browne, Christopher F. Lehnert
Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection. CoRR abs/2302.06039 (2023) - [i42]David Pershouse, Feras Dayoub, Dimity Miller, Niko Sünderhauf:
Addressing the Challenges of Open-World Object Detection. CoRR abs/2303.14930 (2023) - [i41]Lachlan Holden, Feras Dayoub, David Harvey, Tat-Jun Chin:
Federated Neural Radiance Fields. CoRR abs/2305.01163 (2023) - [i40]Xiangyu Shi, Yanyuan Qiao, Qi Wu, Lingqiao Liu, Feras Dayoub:
Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition. CoRR abs/2310.19258 (2023) - [i39]Renjie Wu, Hu Wang, Feras Dayoub, Hsiang-Ting Chen:
Segment Beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation. CoRR abs/2312.08673 (2023) - 2022
- [j14]David Hall
BenchBot environments for active robotics (BEAR): Simulated data for active scene understanding research. Int. J. Robotics Res. 41(3): 259-269 (2022) - [j13]Dimity Miller
Uncertainty for Identifying Open-Set Errors in Visual Object Detection. IEEE Robotics Autom. Lett. 7(1): 215-222 (2022) - [j12]Quazi Marufur Rahman
FSNet: A Failure Detection Framework for Semantic Segmentation. IEEE Robotics Autom. Lett. 7(2): 3030-3037 (2022) - [i38]Jad Abou-Chakra, Feras Dayoub, Niko Sünderhauf:
Implicit Object Mapping With Noisy Data. CoRR abs/2204.10516 (2022) - [i37]Samuel Wilson, Tobias Fischer, Feras Dayoub, Niko Sünderhauf:
Noisy Inliers Make Great Outliers: Out-of-Distribution Object Detection with Noisy Synthetic Outliers. CoRR abs/2208.13930 (2022) - [i36]Jad Abou-Chakra, Feras Dayoub, Niko Sünderhauf:
ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields in Dynamic Scenes. CoRR abs/2211.04041 (2022) - 2021
- [j11]Quazi Marufur Rahman
Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends. IEEE Access 9: 20067-20075 (2021) - [j10]Ben Talbot
Robot Navigation in Unseen Spaces Using an Abstract Map. IEEE Trans. Cogn. Dev. Syst. 13(4): 791-805 (2021) - [c37]Haoyang Zhang, Ying Wang
VarifocalNet: An IoU-Aware Dense Object Detector. CVPR 2021: 8514-8523 - [c36]Olga Moskvyak
Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings. DICTA 2021: 1-8 - [c35]Olga Moskvyak, Frédéric Maire, Feras Dayoub
Semi-supervised Keypoint Localization. ICLR 2021 - [c34]Quazi Marufur Rahman, Niko Sünderhauf
Online Monitoring of Object Detection Performance During Deployment. IROS 2021: 4839-4845 - [c33]Suman Raj Bista
Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM. IROS 2021: 5328-5335 - [c32]Quazi Marufur Rahman, Niko Sünderhauf
Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During Deployment. WACV (Workshops) 2021: 152-160 - [c31]Olga Moskvyak
Keypoint-Aligned Embeddings for Image Retrieval and Re-identification. WACV 2021: 676-685 - [c30]Dimity Miller
Class Anchor Clustering: A Loss for Distance-based Open Set Recognition. WACV 2021: 3569-3577 - [i35]Sourav Garg, Niko Sünderhauf, Feras Dayoub, Douglas Morrison, Akansel Cosgun, Gustavo Carneiro, Qi Wu, Tat-Jun Chin, Ian D. Reid, Stephen Gould, Peter Corke, Michael Milford:
Semantics for Robotic Mapping, Perception and Interaction: A Survey. CoRR abs/2101.00443 (2021) - [i34]Quazi Marufur Rahman, Peter Corke, Feras Dayoub:
Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends. CoRR abs/2101.01364 (2021) - [i33]Olga Moskvyak, Frédéric Maire, Feras Dayoub, Mahsa Baktashmotlagh:
Semi-supervised Keypoint Localization. CoRR abs/2101.07988 (2021) - [i32]Dimity Miller, Niko Sünderhauf, Michael Milford, Feras Dayoub:
Uncertainty for Identifying Open-Set Errors in Visual Object Detection. CoRR abs/2104.01328 (2021) - [i31]Olga Moskvyak, Frédéric Maire, Feras Dayoub, Mahsa Baktashmotlagh:
Going Deeper into Semi-supervised Person Re-identification. CoRR abs/2107.11566 (2021) - [i30]Quazi Marufur Rahman, Niko Sünderhauf, Peter Corke, Feras Dayoub:
FSNet: A Failure Detection Framework for Semantic Segmentation. CoRR abs/2108.08748 (2021) - [i29]Suman Raj Bista, David Hall, Ben Talbot, Haoyang Zhang, Feras Dayoub, Niko Sünderhauf:
Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM. CoRR abs/2109.07748 (2021) - [i28]Samuel Wilson, Niko Sünderhauf, Feras Dayoub:
Hyperdimensional Feature Fusion for Out-Of-Distribution Detection. CoRR abs/2112.05341 (2021) - 2020
- [j9]Sourav Garg
Semantics for Robotic Mapping, Perception and Interaction: A Survey. Found. Trends Robotics 8(1-2): 1-224 (2020) - [c29]Olga Moskvyak
Learning Landmark Guided Embeddings for Animal Re-identification. WACV Workshops 2020: 12-19 - [c28]David Hall
Probabilistic Object Detection: Definition and Evaluation. WACV 2020: 1020-1029 - [i27]Bilal Arain, Feras Dayoub, Paul Rigby, Matthew Dunbabin:
Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse Range Estimation. CoRR abs/2001.00330 (2020) - [i26]Peter Corke, Feras Dayoub, David Hall, John Skinner, Niko Sünderhauf:
What can robotics research learn from computer vision research? CoRR abs/2001.02366 (2020) - [i25]Olga Moskvyak, Frédéric Maire, Feras Dayoub, Mahsa Baktashmotlagh:
Learning landmark guided embeddings for animal re-identification. CoRR abs/2001.02801 (2020) - [i24]Jesse Haviland, Feras Dayoub, Peter Corke:
Predicting Target Feature Configuration of Non-stationary Objects for Grasping with Image-Based Visual Servoing. CoRR abs/2001.05650 (2020) - [i23]Ben Talbot, Feras Dayoub, Peter Corke, Gordon F. Wyeth:
Robot Navigation in Unseen Spaces using an Abstract Map. CoRR abs/2001.11684 (2020) - [i22]Dimity Miller, Niko Sünderhauf, Michael Milford, Feras Dayoub:
Class Anchor Clustering: a Distance-based Loss for Training Open Set Classifiers. CoRR abs/2004.02434 (2020) - [i21]Ben Talbot, David Hall, Haoyang Zhang, Suman Raj Bista, Rohan Smith, Feras Dayoub, Niko Sünderhauf:
BenchBot: Evaluating Robotics Research in Photorealistic 3D Simulation and on Real Robots. CoRR abs/2008.00635 (2020) - [i20]Olga Moskvyak, Frédéric Maire, Feras Dayoub, Mahsa Baktashmotlagh:
Keypoint-Aligned Embeddings for Image Retrieval and Re-identification. CoRR abs/2008.11368 (2020) - [i19]Haoyang Zhang, Ying Wang, Feras Dayoub, Niko Sünderhauf:
VarifocalNet: An IoU-aware Dense Object Detector. CoRR abs/2008.13367 (2020) - [i18]David Hall, Ben Talbot, Suman Raj Bista, Haoyang Zhang, Rohan Smith, Feras Dayoub, Niko Sünderhauf:
The Robotic Vision Scene Understanding Challenge. CoRR abs/2009.05246 (2020) - [i17]Quazi Marufur Rahman, Niko Sünderhauf, Feras Dayoub:
Performance Monitoring of Object Detection During Deployment. CoRR abs/2009.08650 (2020) - [i16]Quazi Marufur Rahman, Niko Sünderhauf, Feras Dayoub:
Online Monitoring of Object Detection Performance Post-Deployment. CoRR abs/2011.07750 (2020) - [i15]Haoyang Zhang, Ying Wang, Feras Dayoub, Niko Sünderhauf:
SWA Object Detection. CoRR abs/2012.12645 (2020)
2010 – 2019
- 2019
- [j8]Niko Sünderhauf
A probabilistic challenge for object detection. Nat. Mach. Intell. 1(9): 443 (2019) - [c27]Dimity Miller, Niko Sünderhauf, Haoyang Zhang, David Hall, Feras Dayoub:
Benchmarking Sampling-based Probabilistic Object Detectors. CVPR Workshops 2019: 42-45 - [c26]Dimity Miller
Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection. ICRA 2019: 2348-2354 - [c25]Quazi Marufur Rahman, Niko Sünderhauf
Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors. IROS 2019: 3748-3753 - [c24]Lucie Halodová, Eliska Dvoráková, Filip Majer, Tomás Vintr, Óscar Martínez Mozos
Predictive and adaptive maps for long-term visual navigation in changing environments. IROS 2019: 7033-7039 - [c23]Peter Corke
What Can Robotics Research Learn from Computer Vision Research? ISRR 2019: 987-1003 - [i14]Olga Moskvyak, Frédéric Maire, Asia O. Armstrong, Feras Dayoub, Mahsa Baktashmotlagh:
Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings. CoRR abs/1902.10847 (2019) - [i13]Quazi Marufur Rahman, Niko Sünderhauf, Feras Dayoub:
Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors. CoRR abs/1903.06391 (2019) - [i12]John Skinner, David Hall
The Probabilistic Object Detection Challenge. CoRR abs/1903.07840 (2019) - 2018
- [j7]David Hall
A rapidly deployable classification system using visual data for the application of precision weed management. Comput. Electron. Agric. 148: 107-120 (2018) - [j6]Ho Seok Ahn, Inkyu Sa, Feras Dayoub
Introduction to the Special Issue on Precision Agricultural Robotics and Autonomous Farming Technologies. IEEE Robotics Autom. Lett. 3(4): 4435-4438 (2018) - [c22]Ahmed Abbas, Frédéric Maire, Sareh Shirazi, Feras Dayoub, Markus Eich:
A Dynamic Planner for Object Assembly Tasks Based on Learning the Spatial Relationships of Its Parts from a Single Demonstration. Australasian Conference on Artificial Intelligence 2018: 759-765 - [c21]Dimity Miller
Dropout Sampling for Robust Object Detection in Open-Set Conditions. ICRA 2018: 1-7 - [c20]Aaron McFadyen
Assisted Control for Semi-Autonomous Power Infrastructure Inspection Using Aerial Vehicles. IROS 2018: 5719-5726 - [i11]David Hall, Feras Dayoub, Tristan Perez, Chris McCool:
A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management. CoRR abs/1801.08613 (2018) - [i10]Aaron McFadyen, Feras Dayoub, Steve Martin, Jason J. Ford, Peter Corke:
Assisted Control for Semi-Autonomous Power Infrastructure Inspection using Aerial Vehicles. CoRR abs/1804.02154 (2018) - [i9]Ho Seok Ahn, Feras Dayoub, Marija Popovic, Bruce A. MacDonald, Roland Siegwart, Inkyu Sa:
An Overview of Perception Methods for Horticultural Robots: From Pollination to Harvest. CoRR abs/1807.03124 (2018) - [i8]Dimity Miller, Feras Dayoub, Michael Milford, Niko Sünderhauf:
Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection. CoRR abs/1809.06006 (2018) - [i7]David Hall, Feras Dayoub, John Skinner, Peter Corke, Gustavo Carneiro, Niko Sünderhauf:
Probability-based Detection Quality (PDQ): A Probabilistic Approach to Detection Evaluation. CoRR abs/1811.10800 (2018) - 2017
- [j5]Owen Bawden
Robot for weed species plant-specific management. J. Field Robotics 34(6): 1179-1199 (2017) - [j4]Inkyu Sa
Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting - Combined Color and 3-D Information. IEEE Robotics Autom. Lett. 2(2): 765-772 (2017) - [c19]Feras Dayoub
Episode-Based Active Learning with Bayesian Neural Networks. CVPR Workshops 2017: 498-500 - [c18]David Hall
Towards unsupervised weed scouting for agricultural robotics. ICRA 2017: 5223-5230 - [c17]David Hall
A transplantable system for weed classification by agricultural robotics. IROS 2017: 5174-5179 - [i6]Inkyu Sa, Christopher F. Lehnert, Andrew English, Chris McCool, Feras Dayoub, Ben Upcroft, Tristan Perez:
Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting - Combined Colour and 3D Information. CoRR abs/1701.08608 (2017) - [i5]David Hall, Feras Dayoub, Jason Kulk, Chris McCool:
Towards Unsupervised Weed Scouting for Agricultural Robotics. CoRR abs/1702.01247 (2017) - [i4]Feras Dayoub, Niko Sünderhauf, Peter I. Corke:
Episode-Based Active Learning with Bayesian Neural Networks. CoRR abs/1703.07473 (2017) - [i3]Dimity Miller, Lachlan Nicholson, Feras Dayoub, Niko Sünderhauf:
Dropout Sampling for Robust Object Detection in Open-Set Conditions. CoRR abs/1710.06677 (2017) - 2016
- [j3]Inkyu Sa, ZongYuan Ge
DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors 16(8): 1222 (2016) - [c16]Christopher McCool
Visual detection of occluded crop: For automated harvesting. ICRA 2016: 2506-2512 - [c15]Niko Sünderhauf
Place categorization and semantic mapping on a mobile robot. ICRA 2016: 5729-5736 - [c14]Ben Talbot
Find my office: Navigating real space from semantic descriptions. ICRA 2016: 5782-5787 - 2015
- [j2]Feras Dayoub
Rubbing Shoulders With Mobile Service Robots. IEEE Access 3: 333-342 (2015) - [c13]Ruth Schulz, Ben Talbot
Robot navigation using human cues: A robot navigation system for symbolic goal-directed exploration. ICRA 2015: 1100-1105 - [c12]Feras Dayoub
Robotic detection and tracking of Crown-of-Thorns starfish. IROS 2015: 1921-1928 - [c11]Niko Sünderhauf
On the performance of ConvNet features for place recognition. IROS 2015: 4297-4304 - [c10]Niko Sünderhauf
Place Recognition with ConvNet Landmarks: Viewpoint-Robust, Condition-Robust, Training-Free. Robotics: Science and Systems 2015 - [c9]David Hall
Evaluation of Features for Leaf Classification in Challenging Conditions. WACV 2015: 797-804 - [i2]Niko Sünderhauf, Feras Dayoub, Sareh Shirazi, Ben Upcroft, Michael Milford:
On the Performance of ConvNet Features for Place Recognition. CoRR abs/1501.04158 (2015) - [i1]Niko Sünderhauf, Feras Dayoub, Sean McMahon, Ben Talbot, Ruth Schulz, Peter I. Corke, Gordon F. Wyeth, Ben Upcroft, Michael Milford:
Place Categorization and Semantic Mapping on a Mobile Robot. CoRR abs/1507.02428 (2015) - 2014
- [c8]Timothy Morris, Feras Dayoub
Multiple map hypotheses for planning and navigating in non-stationary environments. ICRA 2014: 2765-2770 - [c7]Timothy Morris, Feras Dayoub
Simultaneous localization and planning on multiple map hypotheses. IROS 2014: 4531-4536 - 2013
- [c6]Feras Dayoub
Eight Weeks of Episodic Visual Navigation Inside a Non-stationary Environment Using Adaptive Spherical Views. FSR 2013: 379-392 - [c5]Feras Dayoub
Vision-only autonomous navigation using topometric maps. IROS 2013: 1923-1929 - 2011
- [j1]Feras Dayoub
Long-term experiments with an adaptive spherical view representation for navigation in changing environments. Robotics Auton. Syst. 59(5): 285-295 (2011) - [c4]Feras Dayoub, Grzegorz Cielniak, Tom Duckett:
A Sparse Hybrid Map for Vision-Guided Mobile Robots. ECMR 2011: 213-218 - [c3]Feras Dayoub
Long-Term Experiment Using an Adaptive Appearance-Based Map for Visual Navigation by Mobile Robots. TAROS 2011: 400-401
2000 – 2009
- 2009
- [c2]Feras Dayoub, Tom Duckett, Grzegorz Cielniak:
An Adaptive Spherical View Representation for Navigation in Changing Environments. ECMR 2009: 1-6 - 2008
- [c1]Feras Dayoub
An adaptive appearance-based map for long-term topological localization of mobile robots. IROS 2008: 3364-3369
Coauthor Index
Peter I. Corke
aka: Peter Corke
aka: Peter Corke
[c42] [c40] [i49] [i48] [c39] [c38] [i42] [j14] [j13] [j12] [i38] [i37] [i36] [c37] [c34] [c33] [c32] [c30] [i35] [i32] [i30] [i29] [i28] [j9] [c28] [i26] [i22] [i21] [i19] [i18] [i17] [i16] [i15] [j8] [c27] [c26] [c25] [c23] [i13] [i12] [c21] [i8] [i7] [c19] [i4] [i3] [c15] [c11] [c10] [c9] [i2] [i1]
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