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Publications by Hedvig Kjellström

Peer reviewed

Articles

[1]
S. Broomé et al., "Going Deeper than Tracking : A Survey of Computer-Vision Based Recognition of Animal Pain and Emotions," International Journal of Computer Vision, vol. 131, no. 2, pp. 572-590, 2023.
[3]
A. Maki et al., "In Memoriam : Jan-Olof Eklundh," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, pp. 4488-4489, 2022.
[5]
T. Kucherenko et al., "Moving Fast and Slow : Analysis of Representations and Post-Processing in Speech-Driven Automatic Gesture Generation," International Journal of Human-Computer Interaction, vol. 37, no. 14, pp. 1300-1316, 2021.
[8]
P. H. Andersen et al., "Towards Machine Recognition of Facial Expressions of Pain in Horses," Animals, vol. 11, no. 6, 2021.
[9]
M. Klasson, C. Zhang and H. Kjellström, "Using Variational Multi-view Learning for Classification of Grocery Items," Patterns, vol. 1, no. 8, 2020.
[10]
C. Zhang et al., "Advances in Variational Inference," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 8, pp. 2008-2026, 2019.
[11]
K. Stefanov et al., "Modeling of Human Visual Attention in Multiparty Open-World Dialogues," ACM Transactions on Human-Robot Interaction, vol. 8, no. 2, 2019.
[12]
A. Pieropan et al., "Robust and adaptive keypoint-based object tracking," Advanced Robotics, vol. 30, no. 4, pp. 258-269, 2016.
[13]
J. Romero et al., "Extracting Postural Synergies for Robotic Grasping," IEEE Transactions on robotics, vol. 29, no. 6, pp. 1342-1352, 2013.
[14]
J. Romero et al., "Non-parametric hand pose estimation with object context," Image and Vision Computing, vol. 31, no. 8, pp. 555-564, 2013.
[15]
. Sanmohan et al., "Primitive-Based Action Representation and Recognition," Advanced Robotics, vol. 25, no. 6-7, pp. 871-891, 2011.
[16]
H. Kjellström, J. Romero and D. Kragic, "Visual object-action recognition : Inferring object affordances from human demonstration," Computer Vision and Image Understanding, vol. 115, no. 1, pp. 81-90, 2011.
[17]
H. Kjellström and O. Engwall, "Audiovisual-to-articulatory inversion," Speech Communication, vol. 51, no. 3, pp. 195-209, 2009.
[18]
S. Ahlberg et al., "An information fusion demonstrator for tactical intelligence processing in network-based defense," Information Fusion, vol. 8, no. 1, pp. 84-107, 2007.
[19]
O. Engwall et al., "Designing the user interface of the computer-based speech training system ARTUR based on early user tests," Behavior and Information Technology, vol. 25, no. 4, pp. 353-365, 2006.
[20]
D. Ormoneit et al., "Representing cyclic human motion using functional analysis," Image and Vision Computing, vol. 23, no. 14, pp. 1264-1276, 2005.
[21]
H. Sidenbladh and M. J. Black, "Learning the statistics of people in images and video," International Journal of Computer Vision, vol. 54, no. 2-Jan, pp. 181-207, 2003.

Conference papers

[22]
W. Yin et al., "Controllable Motion Synthesis and Reconstruction with Autoregressive Diffusion Models," in 2023 32ND IEEE INTERNATIONAL CONFERENCE ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, RO-MAN, 2023, pp. 1102-1108.
[23]
S. Broomé et al., "Recur, Attend or Convolve? : On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition," in 2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2023, pp. 4188-4198.
[24]
M. B. Colomer et al., "To Adapt or Not to Adapt? : Real-Time Adaptation for Semantic Segmentation," in 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2023, pp. 16502-16513.
[25]
O. Mikheeva et al., "Aligned Multi-Task Gaussian Process," in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 2022, pp. 2970-2988.
[26]
M. Rashid et al., "Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation," in 2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022), 2022, pp. 152-162.
[27]
T. Kucherenko et al., "Multimodal analysis of the predictability of hand-gesture properties," in AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022, pp. 770-779.
[28]
R. Tu et al., "Optimal transport for causal discovery," in ICLR 2022 : 10th International Conference on Learning Representations, International Conference on Learning Representations, 2022.
[29]
R. Nagy et al., "A Framework for Integrating Gesture Generation Models into Interactive Conversational Agents," in 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)., 2021.
[30]
R. Nagy et al., "A framework for integrating gesture generation models into interactive conversational agents," in Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2021, pp. 1767-1769.
[31]
B. Christoffersen et al., "Asymptotically Exact and Fast Gaussian Copula Models for Imputation of Mixed Data Types," in Proceedings of Machine Learning Research, 2021, pp. 870-885.
[32]
M. M. Sorkhei, G. E. Henter and H. Kjellström, "Full-Glow : Fully conditional Glow for more realistic image generation," in Pattern Recognition : 43rd DAGM German Conference, DAGM GCPR 2021, 2021, pp. 697-711.
[33]
J. Mänttäri et al., "Interpreting Video Features : A Comparison of 3D Convolutional Networks and Convolutional LSTM Networks," in 15th Asian Conference on Computer Vision, ACCV 2020, 2021, pp. 411-426.
[34]
T. Kucherenko et al., "Speech2Properties2Gestures : Gesture-Property Prediction as a Tool for Generating Representational Gestures from Speech," in IVA '21 : Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents, 2021, pp. 145-147.
[35]
M. Rashid, H. Kjellström and Y. J. Lee, "Action Graphs : Weakly-supervised Action Localization with Graph Convolution Networks," in 2020 ieee winter conference on applications of computer vision (wacv), 2020, pp. 604-613.
[36]
R. Tu et al., "Causal Discovery in the Presence of Missing Data," in 22nd international conference on artificial intelligence and statistics, vol 89, 2020.
[37]
T. Kucherenko et al., "Gesticulator : A framework for semantically-aware speech-driven gesture generation," in ICMI '20: Proceedings of the 2020 International Conference on Multimodal Interaction, 2020.
[38]
C. Ringqvist et al., "Interpolation in Auto Encoders with Bridge Processes," in Proceedings of the 25th International Conference on Pattern Recognition, ICPR 2020, 2020.
[40]
J. Wenger, H. Kjellström and R. Triebel, "Non-Parametric Calibration for Classification," in International Conference on Artificial Intelligence and Statistics, Vol 108, 2020.
[41]
P. L. Dovesi et al., "Real-Time Semantic Stereo Matching," in Proceedings - IEEE International Conference on Robotics and Automation, 2020, pp. 10780-10787.
[42]
K. Håkansson et al., "Robot-assisted detection of subclinical dementia : progress report and preliminary findings," in In 2020 Alzheimer's Association International Conference. ALZ., 2020.
[43]
M. Klasson, C. Zhang and H. Kjellström, "A hierarchical grocery store image dataset with visual and semantic labels," in Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, 2019, pp. 491-500.
[44]
T. Kucherenko et al., "Analyzing Input and Output Representations for Speech-Driven Gesture Generation," in 19th ACM International Conference on Intelligent Virtual Agents, 2019.
[45]
S. Eriksson et al., "Dancing with Drones : Crafting Novel Artistic Expressions through Intercorporeality," in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019, pp. 617:1-617:12.
[46]
S. Broomé et al., "Dynamics are important for the recognition of equine pain in video," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019.
[47]
R. Tu et al., "Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation," in Advances in neural information processing systems 32 (NIPS 2019), 2019.
[48]
T. Kucherenko et al., "On the Importance of Representations for Speech-Driven Gesture Generation : Extended Abstract," in International Conference on Autonomous Agents and Multiagent Systems (AAMAS '19), May 13-17, 2019, Montréal, Canada, 2019, pp. 2072-2074.
[49]
J. Butepage, H. Kjellström and D. Kragic, "Predicting the what and how - A probabilistic semi-supervised approach to multi-task human activity modeling," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019, pp. 2923-2926.
[50]
P. Wolfert et al., "Should Beat Gestures Be Learned Or Designed? : A Benchmarking User Study," in ICDL-EPIROB 2019 : Workshop on Naturalistic Non-Verbal and Affective Human-Robot Interactions, 2019.
[51]
C. Hamesse et al., "Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation," in Proceedings of Machine Learning Research 106, 2019.
[52]
J. Butepage, H. Kjellström and D. Kragic, "Anticipating many futures : Online human motion prediction and generation for human-robot interaction," in 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2018, pp. 4563-4570.
[53]
P. Haubro Andersen et al., "Can a Machine Learn to See Horse Pain? : An Interdisciplinary Approach Towards Automated Decoding of Facial Expressions of Pain in the Horse," in Measuring Behavior 2018 - 11th International Conference on Methods and Techniques in Behavioral Research 6-8 June, 2018, 2018.
[54]
O. Mikheeva, C. H. Ek and H. Kjellström, "Perceptual facial expression representation," in Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018, 2018, pp. 179-186.
[56]
K. Karipidou et al., "Computer Analysis of Sentiment Interpretation in Musical Conducting," in Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017, 2017, pp. 400-405.
[57]
J. Butepage et al., "Deep representation learning for human motion prediction and classification," in 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017, pp. 1591-1599.
[58]
C. Zhang, H. Kjellström and S. Mandt, "Determinantal point processes for mini-batch diversification," in Uncertainty in Artificial Intelligence - Proceedings of the 33rd Conference, UAI 2017, 2017.
[59]
Y. Zhang, J. Beskow and H. Kjellström, "Look but Don’t Stare : Mutual Gaze Interaction in Social Robots," in 9th International Conference on Social Robotics, ICSR 2017, 2017, pp. 556-566.
[60]
A. Eriksson and H. Kjellström, "A formal approach to anomaly detection," in ICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods, 2016, pp. 317-326.
[61]
S. Caccamo et al., "Active perception and modeling of deformable surfaces using Gaussian processes and position-based dynamics," in IEEE-RAS International Conference on Humanoid Robots, 2016, pp. 530-537.
[62]
C. Zhang, S. Mandt and H. Kjellström, "Balanced Population Stochastic Variational Inference," in NIPS Workshop on Advances in Approximate Bayesian Inference, 2016.
[63]
A. Qu et al., "Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation," in NIPS Workshop on Machine Learning for Health, 2016.
[64]
C. Zhang et al., "Diagnostic Prediction Using Discomfort Drawing with IBTM," in Machine Learning in Health Care, 2016.
[65]
C. Zhang, H. Kjellström and B. C. Bertilson, "Diagnostic Prediction Using Discomfort Drawings," in NIPS Workshop on Machine Learning for Health, 2016.
[66]
C. Zhang, H. Kjellström and C. H. Ek, "Inter-battery topic representation learning," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, pp. 210-226.
[67]
A. Pieropan et al., "Robust tracking of unknown objects through adaptive size estimation and appearance learning," in Proceedings - IEEE International Conference on Robotics and Automation, 2016, pp. 559-566.
[68]
J. Butepage, H. Kjellström and D. Kragic, "Social Affordance Tracking over Time - A Sensorimotor Account of False-Belief Tasks," in Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016, 2016, pp. 1014-1019.
[69]
R. Güler et al., "Estimating the Deformability of Elastic Materials using Optical Flow and Position-based Dynamics," in Humanoid Robots (Humanoids), 2015 IEEE-RAS 15th International Conference on, 2015, pp. 965-971.
[70]
A. Pieropan et al., "Robust 3D tracking of unknown objects," in Proceedings - IEEE International Conference on Robotics and Automation, 2015, pp. 2410-2417.
[71]
A. Pieropan et al., "Audio-Visual Classification and Detection of Human Manipulation Actions," in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), 2014, pp. 3045-3052.
[72]
A. Pieropan, C. H. Ek and H. Kjellström, "Recognizing Object Affordances in Terms of Spatio-Temporal Object-Object Relationships," in Humanoid Robots (Humanoids), 2014 14th IEEE-RAS International Conference on, 2014, pp. 52-58.
[73]
A. Pieropan et al., "Robust Tracking through Learning," in 32nd Annual Conference of the Robotics Society of Japan, 2014, 2014.
[74]
A. Pieropan and H. Kjellström, "Unsupervised object exploration using context," in The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 2014 RO-MAN, 2014.
[75]
D. Geronimo and H. Kjellström, "Unsupervised surveillance video retrieval based on human action and appearance," in Proceedings - International Conference on Pattern Recognition, 2014, pp. 4630-4635.
[76]
C. Zhang, D. Song and H. Kjellström, "Contextual Modeling with Labeled Multi-LDA," in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013, pp. 2264-2271.
[77]
C. Zhang et al., "Factorized Topic Models," in 1st International Conference on Learning Representations, ICLR 2013, 2-4 May 2013, Scottsdale, United States, 2013.
[78]
A. Pieropan, C. H. Ek and H. Kjellström, "Functional Object Descriptors for Human Activity Modeling," in 2013 IEEE International Conference on Robotics and Automation (ICRA), 2013, pp. 1282-1289.
[79]
A. Thippur, C. H. Ek and H. Kjellström, "Inferring hand pose : A comparative study of visual shape features," in 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013, 2013, p. 6553698.
[80]
M. Hjelm et al., "Sparse Summarization of Robotic Grasping Data," in 2013 IEEE International Conference on Robotics and Automation (ICRA), 2013, pp. 1082-1087.
[81]
C. Zhang et al., "Supervised Hierarchical Dirichlet Processes with Variational Inference," in 2013 IEEE International Conference on Computer Vision Workshops (ICCVW), 2013, pp. 254-261.
[82]
F. T. Pokorny et al., "Persistent Homology for Learning Densities with Bounded Support," in Advances in Neural Information Processing Systems 25 : 26th Annual Conference on Neural Information Processing Systems 2012, 2012, pp. 1817-1825.
[83]
F. T. Pokorny et al., "Topological Constraints and Kernel-Based Density Estimation," in Advances in Neural Information Processing Systems 25, Workshop on Algebraic Topology and Machine Learning, December 8th, Nevada, USA, 2012.
[84]
J. Romero, H. Kjellström and D. Kragic, "Hands in Action : Real-Time 3D Reconstruction of Hands in Interaction with Objects," in 2010 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2010, pp. 458-463.
[85]
J. Romero et al., "Spatio-Temporal Modeling of Grasping Actions," in IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, pp. 2103-2108.
[86]
H. Kjellström, D. Kragic and M. J. Black, "Tracking People Interacting with Objects," in 2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2010, pp. 747-754.
[87]
M. Do et al., "Grasp recognition and mapping on humanoid robots," in 9th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS09, 2009, pp. 465-471.
[88]
J. Romero, H. Kjellström and D. Kragic, "Modeling and Evaluation of Human-to-Robot Mapping of Grasps," in ICAR : 2009 International Conference on Advanced Robotics, 2009, pp. 228-233.
[89]
J. Romero, H. Kjellström and D. Kragic, "Monocular Real-Time 3D Articulated Hand Pose Estimation," in 9th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS09, 2009, pp. 87-92.
[90]
J. Romero, H. Kjellström and D. Kragic, "Human-to-Robot Mapping of Grasps," in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Nice, France. September 22 - 26 2008, 2008.
[91]
H. Kjellström et al., "Simultaneous Visual Recognition of Manipulation Actions and Manipulated Objects," in Computer Vision - Eccv 2008, Pt Ii, Proceedings, 2008, pp. 336-349.
[92]
H. Kjellström, J. Romero and D. Kragic, "Visual Recognition of Grasps for Human-to-Robot Mapping," in 2008 IEEE/RSJ International Conference On Robots And Intelligent Systems, Vols 1-3, Conference Proceedings, 2008, pp. 3192-3199.
[93]
H. Kjellström et al., "Audio-visual phoneme classification for pronunciation training applications," in INTERSPEECH 2007 : 8TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION, 2007, pp. 57-60.
[94]
O. Engwall et al., "Feedback management in the pronunciation training system ARTUR," in Proceedings of CHI 2006, 2006, pp. 231-234.
[95]
H. Kjellström, O. Engwall and O. Bälter, "Reconstructing Tongue Movements from Audio and Video," in INTERSPEECH 2006 AND 9TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING, Vol. 1-5, 2006, pp. 2238-2241.
[96]
H. Sidenbladh, P. Svenson and J. Schubert, "Comparing future situation pictures," in 2005 7th International Conference on Information Fusion (FUSION), Vols 1 and 2, 2005, pp. 963-968.
[97]
E. Eriksson et al., "Design Recommendations for a Computer-Based Speech Training System Based on End User Interviews," in Proceedings of the Tenth International Conference on Speech and Computers, 2005, pp. 483-486.
[98]
J. Schubert and H. Sidenbladh, "Sequential clustering with particle filters - estimating the number of clusters from data," in 2005 7th International Conference on Information Fusion (FUSION), Vols 1 and 2, 2005, pp. 122-129.
[99]
J. Grahn and H. Kjellström, "Using SVM for efficient detection of human motion," in 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS, 2005, pp. 231-238.
[100]
[101]
H. Sidenbladh, P. Svenson and J. Schubert, "Comparing multi-target trackers on different force unit levels," in SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XIII, 2004, pp. 306-314.
[102]
H. Sidenbladh, "Detecting human motion with support vector machines," in PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, 2004, pp. 188-191.
[103]
J. Schubert et al., "Methods and system design of the IFD03 information fusion demonstrator," in Ninth International Command and Control Research and Technology Symposium, 2004, pp. 1-29.
[104]
S. Ahlberg et al., "The IFD03 information fusion demonstrator," in Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004, 2004, pp. 936-943.
[105]
P. Svenson and H. Sidenbladh, "Determining possible avenues of approach using ANTS," in FUSION 2003 : PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE OF INFORMATION FUSION, VOLS 1 AND 2, 2003, pp. 1110-1117.
[106]
H. Sidenbladh, "Multi-target particle filtering for the probability hypothesis density," in FUSION 2003 : PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE OF INFORMATION FUSION, VOLS 1 AND 2, 2003, pp. 800-806.
[107]
H. Sidenbladh and S.-L. Wirkander, "Tracking random sets of vehicles in terrain," in Proceedings of the 2003 IEEE Workshop on Multi-Object Tracking, 2003.
[108]
H. Sidenbladh, M. J. Black and L. Sigal, "Implicit probabilistic models of human motion for synthesis and tracking," in COMPUTER VISON - ECCV 2002, PT 1, 2002, pp. 784-800.
[109]
M. Bray, H. Sidenbladh and J.-O. Eklundh, "Recognition of gestures in the context of speech," in 16th International Conference on Pattern Recognition, 2002. Proceedings., 2002.
[110]
D. Ormoneit et al., "Learning and tracking cyclic human motion," in Advances in Neural Information Processing Systems 13, 2001.
[111]
H. Sidenbladh and M. J. Black, "Learning image statistics for Bayesian tracking," in Proceedings of the IEEE International Conference on Computer Vision, 2001, pp. 709-716.
[112]
H. Sidenbladh, F. De la Torre and M. J. Black, "A framework for modeling the appearance of 3D articulated figures," in IEEE International Conference on Automatic Face and Gesture Recognition, 2000.
[113]
D. Ormoneit et al., "Learning and tracking human motion using functional analysis," in IEEE Workshop on Human Modeling, Analysis and Synthesis, 2000.
[114]
D. Ormoneit, H. Sidenbladh and M. J. Black, "Stochastic modeling and tracking of human motion," in Learning 2000, 2000.
[115]
H. Sidenbladh, M. J. Black and D. J. Fleet, "Stochastic tracking of 3D human figures using 2D image motion," in European Conference on Computer Vision, 2000.
[116]
H. Sidenbladh, D. Kragic and H. I. Christensen, "Person following behaviour for a mobile robot," in Proceedings - IEEE International Conference on Robotics and Automation, 1999, pp. 670-675.

Chapters in books

[117]
C. Zhang and H. Kjellström, "How to Supervise Topic Models," in Computer Vision - ECCV 2014 Workshops : Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II, Agapito, Bronstein, Rother Ed., Zurich : Springer Publishing Company, 2014, pp. 500-515.

Non-peer reviewed

Conference papers

[118]
S. B. Wang et al., "Multimodal communication error detection for driver-car interaction," in ICINCO 2007: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL RA-1: ROBOTICS AND AUTOMATION, VOL 1, 2007, pp. 365-371.

Chapters in books

[119]
H. Kjellström, "Contextual Action Recognition," in Visual Analysis of Humans : Looking at People, T. B. Moeslund, A. Hilton, V. Krüger and L. Sigal Ed., : Springer, 2011, pp. 355-376.
[120]
H. Kjellström, "Datorer som ser människor," in Sinnen, signaler och tolkningar av verkligheten, Lindberg, Bo Ed., Göteborg : Kungliga vetenskaps- och vitterhetssamhället, 2007.

Other

[123]
J. Bütepage, H. Kjellström and D. Kragic, "A Probabilistic Semi-Supervised Approach to Multi-Task Human Activity Modeling," (Manuscript).
[124]
M. Klasson, H. Kjellström and C. Zhang, "Learn the Time to Learn : Replay Scheduling in Continual Learning," (Manuscript).
[125]
M. Klasson, H. Kjellström and C. Zhang, "Policy Learning for Replay Scheduling in Continual Learning," (Manuscript).
[126]
A. Pieropan et al., "Robust 3D tracking of unknown objects," (Manuscript).
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