1 code implementation • 7 Jul 2023 • Vladan Stojnić, Zakaria Laskar, Giorgos Tolias
In this work, we present an approach that leverages three highly synergistic components, which are identified as key ingredients: joint classifier training with inliers and outliers, semi-supervised learning through pseudo-labeling, and model ensembling.
no code implementations • 5 May 2023 • Shuzhe Wang, Zakaria Laskar, Iaroslav Melekhov, Xiaotian Li, Yi Zhao, Giorgos Tolias, Juho Kannala
In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image.
no code implementations • 10 Oct 2021 • Iaroslav Melekhov, Zakaria Laskar, Xiaotian Li, Shuzhe Wang, Juho Kannala
Fully-supervised CNN-based approaches for learning local image descriptors have shown remarkable results in a wide range of geometric tasks.
1 code implementation • ICCV 2021 • Shuzhe Wang, Zakaria Laskar, Iaroslav Melekhov, Xiaotian Li, Juho Kannala
For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component.
no code implementations • 10 Jul 2020 • Zakaria Laskar, Juho Kannala
In low training sample settings, our approach outperforms the fully supervised approach on two challenging image retrieval datasets, ROxford5k and RParis6k \cite{Roxf} with the least possible teacher supervision.
no code implementations • 15 Apr 2019 • Zakaria Laskar, Iaroslav Melekhov, Hamed R. -Tavakoli, Juha Ylioinas, Juho Kannala
The main contribution is a geometric correspondence verification approach for re-ranking a shortlist of retrieved database images based on their dense pair-wise matching with the query image at a pixel level.
no code implementations • 24 Jan 2019 • Zakaria Laskar, Juho Kannala
Convolutional neural networks (CNNs) have been successfully applied to solve the problem of correspondence estimation between semantically related images.
no code implementations • 24 Jan 2019 • Zakaria Laskar, Hamed R. -Tavakoli, Juho Kannala
The problem is posed as finding the geometric transformation that aligns a given image pair.
no code implementations • 31 Jul 2017 • Zakaria Laskar, Iaroslav Melekhov, Surya Kalia, Juho Kannala
The camera location for the query image is obtained via triangulation from two relative translation estimates using a RANSAC based approach.
1 code implementation • 3 Mar 2017 • Zakaria Laskar, Juho Kannala
Particularly, we show that by making the CNN pay attention on the ROI while extracting query image representation leads to significant improvement over the baseline methods on challenging Oxford5k and Paris6k datasets.