Research
My research focuses on the intersection of self-supervised learning, video understanding, and vision-language models.
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TVBench: Redesigning Video-Language Evaluation
Daniel Cores*, Michael Dorkenwald*, Manuel Mucientes, Cees G. M. Snoek, Yuki M. Asano
ArXiv 2024
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GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models
M. Jehanzeb Mirza, Mengjie Zhao, Zhuoyuan Mao, Sivan Doveh, Wei Lin, Paul Gavrikov, Michael Dorkenwald, Shiqi Yang, Saurav Jha, Hiromi Wakaki, Yuki Mitsufuji, Horst Possegger, Rogerio Feris, Leonid Karlinsky, James Glass
ArXiv 2024
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SIGMA: Sinkhorn-Guided Masked Video Modeling
Mohammadreza Salehi*, Michael Dorkenwald*, Fida Mohammad Thoker*, Efstratios Gavves, Cees Snoek, Yuki M. Asano
Accepted at ECCV 2024
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PIN: Positional Insert Unlocks Object Localisation Abilities in VLMs
Michael Dorkenwald, Nimrod Barazani, Cees Snoek*, Yuki M. Asano*
CVPR 2024
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SCVRL: Shuffled Contrastive Video Representation Learning
Michael Dorkenwald, Fanyi Xiao, Biagio Brattoli, Joseph Tighe, Davide Modolo
CVPR 2022 I3D-IVU workshop
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iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis
Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer
ICCV 2021
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Stochastic Image-to-Video Synthesis using cINNs
Michael Dorkenwald, Timo Milbich, Andreas Blattmann, Robin Rombach, Konstantinos G. Derpanis, Björn Ommer
CVPR 2021
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Behavior-Driven Synthesis of Human Dynamics
Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer
CVPR 2021
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Understanding Object Dynamics for Interactive Image-to-Video Synthesis
Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer
CVPR 2021
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Unsupervised behaviour analysis and magnification (uBAM) using deep learning
Biagio Brattoli*, Uta Buechler*, Michael Dorkenwald, Philipp Reiser, Lineard Filli, Fritjof Helmchen, Anna-Sophia Wahl, Björn Ommer
Nature Machine Intelligence
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