| Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning FM Serra Bragança, S Broomé, M Rhodin, S Björnsdóttir, V Gunnarsson, ... Scientific reports 10 (1), 17785, 2020 | 49 | 2020 |
| Towards machine recognition of facial expressions of pain in horses PH Andersen, S Broomé, M Rashid, J Lundblad, K Ask, Z Li, E Hernlund, ... Animals 11 (6), 1643, 2021 | 47 | 2021 |
| Going deeper than tracking: a survey of computer-vision based recognition of animal pain and affective states S Broomé, M Feighelstein, A Zamansky, GC Lencioni, PH Andersen, ... International Journal of Computer Vision, 2022 | 46* | 2022 |
| Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks J Mänttäri, S Broomé, J Folkesson, H Kjellström Computer Vision - ACCV 2020, 15th Asian Conference on Computer Vision, 2020 | 38 | 2020 |
| Dynamics are Important for the Recognition of Equine Pain in Video S Broomé, KB Gleerup, PH Andersen, H Kjellström IEEE Conference on Computer Vision and Pattern Recognition, 2019 | 33 | 2019 |
| hSMAL: Detailed horse shape and pose reconstruction for motion pattern recognition C Li, N Ghorbani, S Broomé, M Rashid, MJ Black, E Hernlund, ... CVPR Workshop on Computer Vision for Animal Behavior Tracking and Modeling, 2021 | 22 | 2021 |
| Sharing pain: Using pain domain transfer for video recognition of low grade orthopedic pain in horses S Broomé, K Ask, M Rashid-Engström, P Haubro Andersen, H Kjellström PloS one 17 (3), e0263854, 2022 | 19* | 2022 |
| Can a Machine Learn to See Horse Pain? An Interdisciplinary Approach Towards Automated Decoding of Facial Expressions of Pain in the Horse PH Andersen, KB Gleerup, J Wathan, B Coles, H Kjellström, S Broomé, ... Measuring Behavior 2018, 2018 | 16 | 2018 |
| Recur, attend or convolve? On whether temporal modeling matters for cross-domain robustness in action recognition S Broomé, E Pokropek, B Li, H Kjellström Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 13 | 2023 |
| Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation M Rashid, S Broomé, K Ask, E Hernlund, PH Andersen, H Kjellström, ... IEEE Winter Conference on Applications of Computer Vision, 2022 | 12 | 2022 |
| Automated detection of equine facial action units Z Li, S Broomé, PH Andersen, H Kjellström arXiv preprint arXiv:2102.08983, 2021 | 9 | 2021 |
| What should I annotate? An automatic tool for finding video segments for EquiFACS annotation M Rashid, S Broome, PH Andersen, KB Gleerup, YJ Lee Measuring Behavior, 2018 | 9 | 2018 |
| Objectively recognizing human activity in body-worn sensor data with (more or less) deep neural networks S Broomé Robotics, Perception and Learning; KTH Royal Institute of Technology, 2017 | 2 | 2017 |
| A PDE Perspective on Climate Modeling S Broomé, J Ridenour Department of Mathematics, KTH Royal Institute of Technology, 2014 | 2 | 2014 |
| Predictive Modeling of Equine Activity Budgets Using a 3D Skeleton Reconstructed from Surveillance Recordings E Pokropek, S Broomé, PH Andersen, H Kjellström CVPR Workshop on Computer Vision for Animal Behavior Tracking and Modeling, 2023 | 1 | 2023 |
| Learning Spatiotemporal Features in Low-Data and Fine-Grained Action Recognition with an Application to Equine Pain Behavior S Broomé KTH Royal Institute of Technology, 2022 | | 2022 |
| [Re] Unsupervised Scalable Representation Learning for Multivariate Time Series F Liljefors, MM Sorkhei, S Broomé ReScience C 6 (NeurIPS 2019 Reproducibility Challenge), 2020 | | 2020 |
Hedvig Kjellström (Sidenbladh)Professor at KTH Royal Institute of Technology, SwedenVerified email at kth.se
