activity
20182022
most citedContrastive Language-Action Pre-training for Temporal Localization

5 citations · 6 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV20225 cited

Contrastive Language-Action Pre-training for Temporal Localization

Mengmeng Xu, Erhan Gundogdu, Maksim Lapin +3

Long-form video understanding requires designing approaches that are able to temporally localize activities or language. End-to-end training for such tasks is limited by the comput…

cs.CV20211 cited

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

Amaia Salvador, Erhan Gundogdu, Loris Bazzani +1

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 cooki…

cs.CV2020

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

Erhan Gundogdu, Victor Constantin, Shaifali Parashar +4

In this paper, we tackle the problem of static 3D cloth draping on virtual human bodies. We introduce a two-stream deep network model that produces a visually plausible draping of…

cs.CV2019

Shape Reconstruction by Learning Differentiable Surface Representations

Jan Bednarik, Shaifali Parashar, Erhan Gundogdu +2

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 represent…

cs.CV2019

Quadruplet Selection Methods for Deep Embedding Learning

Kaan Karaman, Erhan Gundogdu, Aykut Koc +1

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

cs.CV2018

GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping

Erhan Gundogdu, Victor Constantin, Amrollah Seifoddini +3

While Physics-Based Simulation (PBS) can accurately drape a 3D garment on a 3D body, it remains too costly for real-time applications, such as virtual try-on. By contrast, inferenc…