Search Results for author: Erhan Gundogdu

Found 9 papers, 4 papers with code

iEdit: Localised Text-guided Image Editing with Weak Supervision

no code implementations10 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.

Contrastive Learning Descriptive +1

Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning

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.

Cross-Modal Retrieval Retrieval +1

GarNet++: Improving Fast and Accurate Static3D Cloth Draping by Curvature Loss

no code implementations20 Jul 2020 Erhan Gundogdu, Victor Constantin, Shaifali Parashar, Amrollah Seifoddini, Minh Dang, Mathieu Salzmann, Pascal Fua

We introduce a two-stream deep network model that produces a visually plausible draping of a template cloth on virtual 3D bodies by extracting features from both the body and garment shapes.

Shape Reconstruction by Learning Differentiable Surface Representations

1 code implementation CVPR 2020 Jan Bednarik, Shaifali Parashar, Erhan Gundogdu, Mathieu Salzmann, Pascal Fua

Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations.

Quadruplet Selection Methods for Deep Embedding Learning

no code implementations22 Jul 2019 Kaan Karaman, Erhan Gundogdu, Aykut Koc, A. Aydin Alatan

Recognition of objects with subtle differences has been used in many practical applications, such as car model recognition and maritime vessel identification.

feature selection Multi-Task Learning

Good Features to Correlate for Visual Tracking

1 code implementation20 Apr 2017 Erhan Gundogdu, A. Aydin Alatan

The proposed learning framework enables the network model to be flexible for a custom design.

General Classification Object +2

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