Search Results for author: Theo Gevers

Found 22 papers, 8 papers with code

Joint 3D Layout and Depth Prediction from a Single Indoor Panorama Image

no code implementations ECCV 2020 Wei Zeng, Sezer Karaoglu, Theo Gevers

Leveraging the layout depth map as an intermediate representation, our proposed method outperforms existing methods for both panorama layout prediction and depth estimation.

Depth Estimation

Spatio-temporal Features for Generalized Detection of Deepfake Videos

no code implementations22 Oct 2020 Ipek Ganiyusufoglu, L. Minh Ngô, Nedko Savov, Sezer Karaoglu, Theo Gevers

In this paper, we empirically show that existing approaches on image and sequence classifiers generalize poorly to new manipulation techniques.

DeepFake Detection Face Swapping

Novel View Synthesis from Single Images via Point Cloud Transformation

1 code implementation17 Sep 2020 Hoang-An Le, Thomas Mensink, Partha Das, Theo Gevers

In this paper the argument is made that for true novel view synthesis of objects, where the object can be synthesized from any viewpoint, an explicit 3D shape representation isdesired.

3D Shape Representation Novel View Synthesis

Physics-based Shading Reconstruction for Intrinsic Image Decomposition

no code implementations3 Sep 2020 Anil S. Baslamisli, Yang Liu, Sezer Karaoglu, Theo Gevers

We investigate the use of photometric invariance and deep learning to compute intrinsic images (albedo and shading).

Intrinsic Image Decomposition

Multi-Loss Weighting with Coefficient of Variations

1 code implementation3 Sep 2020 Rick Groenendijk, Sezer Karaoglu, Theo Gevers, Thomas Mensink

In this paper, we propose a weighting scheme based on the coefficient of variations and set the weights based on properties observed while training the model.

Monocular Depth Estimation Multi-Task Learning +1

Kinship Identification through Joint Learning Using Kinship Verification Ensembles

1 code implementation ECCV 2020 Wei Wang, ShaoDi You, Sezer Karaoglu, Theo Gevers

The experiments further show significant performance improvement of kinship verification when trained on the same dataset with more realistic distributions.

On the Benefit of Adversarial Training for Monocular Depth Estimation

1 code implementation29 Oct 2019 Rick Groenendijk, Sezer Karaoglu, Theo Gevers, Thomas Mensink

For the quality of the image reconstruction and disparity prediction, a combination of different losses is used, including L1 image reconstruction losses and left-right disparity smoothness.

Image Reconstruction Monocular Depth Estimation

Improving Face Detection Performance with 3D-Rendered Synthetic Data

no code implementations18 Dec 2018 Jian Han, Sezer Karaoglu, Hoang-An Le, Theo Gevers

In this paper, we provide a synthetic data generator methodology with fully controlled, multifaceted variations based on a new 3D face dataset (3DU-Face).

Data Augmentation Face Detection

Color Constancy by GANs: An Experimental Survey

no code implementations7 Dec 2018 Partha Das, Anil S. Baslamisli, Yang Liu, Sezer Karaoglu, Theo Gevers

In this paper, we formulate the color constancy task as an image-to-image translation problem using GANs.

Color Constancy Image-to-Image Translation +1

Automatic Generation of Dense Non-rigid Optical Flow

1 code implementation5 Dec 2018 Hoàng-Ân Lê, Tushar Nimbhorkar, Thomas Mensink, Anil S. Baslamisli, Sezer Karaoglu, Theo Gevers

There hardly exists any large-scale datasets with dense optical flow of non-rigid motion from real-world imagery as of today.

Optical Flow Estimation

Three for one and one for three: Flow, Segmentation, and Surface Normals

1 code implementation19 Jul 2018 Hoang-An Le, Anil S. Baslamisli, Thomas Mensink, Theo Gevers

Optical flow, semantic segmentation, and surface normals represent different information modalities, yet together they bring better cues for scene understanding problems.

Optical Flow Estimation Scene Understanding +1

CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition

no code implementations CVPR 2018 Anil S. Baslamisli, Hoang-An Le, Theo Gevers

On the other hand, recent research use deep learning models as in-and-out black box and do not consider the well-established, traditional image formation process as the basis of their intrinsic learning process.

Intrinsic Image Decomposition

Detect2Rank : Combining Object Detectors Using Learning to Rank

no code implementations26 Dec 2014 Sezer Karaoglu, Yang Liu, Theo Gevers

Experiments on the PASCAL VOC07 and VOC10 datasets show that the proposed method significantly outperforms single object detectors, DPM (8. 4%), CN (6. 8%) and EES (17. 0%) on VOC07 and DPM (6. 5%), CN (5. 5%) and EES (16. 2%) on VOC10.

Learning-To-Rank Object Detection

Road Detection by One-Class Color Classification: Dataset and Experiments

no code implementations11 Dec 2014 Jose M. Alvarez, Theo Gevers, Antonio M. Lopez

These algorithms reduce the effect of lighting variations and weather conditions by exploiting the discriminant/invariant properties of different color representations.

Autonomous Driving Classification +1

Evaluation of Color STIPs for Human Action Recognition

no code implementations CVPR 2013 Ivo Everts, Jan C. van Gemert, Theo Gevers

Existing STIP-based action recognition approaches operate on intensity representations of the image data.

Action Recognition

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