no code implementations • 10 May 2023 • Rumeysa Bodur, Erhan Gundogdu, Binod Bhattarai, Tae-Kyun Kim, Michael Donoser, Loris Bazzani
We propose a novel learning method for text-guided image editing, namely \texttt{iEdit}, that generates images conditioned on a source image and a textual edit prompt.
no code implementations • 26 Apr 2022 • Mengmeng Xu, Erhan Gundogdu, Maksim Lapin, Bernard Ghanem, Michael Donoser, Loris Bazzani
Long-form video understanding requires designing approaches that are able to temporally localize activities or language.
Contrastive Learning Few Shot Temporal Action Localization +3
1 code implementation • CVPR 2021 • Amaia Salvador, Erhan Gundogdu, Loris Bazzani, Michael Donoser
Cross-modal recipe retrieval has recently gained substantial attention due to the importance of food in people's lives, as well as the availability of vast amounts of digital cooking recipes and food images to train machine learning models.
Ranked #6 on Cross-Modal Retrieval on Recipe1M
1 code implementation • ICCV 2021 • Yuxin Hou, Eleonora Vig, Michael Donoser, Loris Bazzani
Interactive retrieval for online fashion shopping provides the ability of changing image retrieval results according to the user feedback.
no code implementations • CVPR 2014 • Michael Donoser, Dieter Schmalstieg
The prevalent approach to image-based localization is matching interest points detected in the query image to a sparse 3D point cloud representing the known world.
no code implementations • CVPR 2014 • Michael Donoser, Dieter Schmalstieg
The state-of-the-art in image segmentation builds hierarchical segmentation structures based on analyzing local feature cues in spectral settings.
no code implementations • CVPR 2013 • Michael Donoser, Horst Bischof
In this paper we revisit diffusion processes on affinity graphs for capturing the intrinsic manifold structure defined by pairwise affinity matrices.
no code implementations • CVPR 2013 • Paul Wohlhart, Martin Kostinger, Michael Donoser, Peter M. Roth, Horst Bischof
The development of complex, powerful classifiers and their constant improvement have contributed much to the progress in many fields of computer vision.