no code implementations • 11 Apr 2024 • Simon Schrodi, David T. Hoffmann, Max Argus, Volker Fischer, Thomas Brox
This revealed that the driving factor behind both, the modality gap and the object bias, is the information imbalance between images and captions.
no code implementations • 19 Oct 2023 • David T. Hoffmann, Simon Schrodi, Nadine Behrmann, Volker Fischer, Thomas Brox
In this work, we study rapid, step-wise improvements of the loss in transformers when being confronted with multi-step decision tasks.
1 code implementation • 27 Jan 2022 • David T. Hoffmann, Nadine Behrmann, Juergen Gall, Thomas Brox, Mehdi Noroozi
This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a new member in the family of InfoNCE losses that preserves a ranked ordering of positive samples.
1 code implementation • CVPR 2021 • Priyanka Patel, Chun-Hao P. Huang, Joachim Tesch, David T. Hoffmann, Shashank Tripathi, Michael J. Black
Additionally, we fine-tune methods on AGORA and show improved performance on both AGORA and 3DPW, confirming the realism of the dataset.
2 code implementations • 24 Oct 2019 • Anurag Ranjan, David T. Hoffmann, Dimitrios Tzionas, Siyu Tang, Javier Romero, Michael J. Black
Therefore, we develop a dataset of multi-human optical flow and train optical flow networks on this dataset.
2 code implementations • 2 Aug 2019 • David T. Hoffmann, Dimitrios Tzionas, Micheal J. Black, Siyu Tang
Here we explore two variations of synthetic data for this challenging problem; a dataset with purely synthetic humans and a real dataset augmented with synthetic humans.