8 citations · 11 across the 10 of their papers we have counts for
10 papers · 1 filter
Ukrainian Visual Word Sense Disambiguation Benchmark
Yurii Laba, Yaryna Mohytych, Ivanna Rohulia +4
This study presents a benchmark for evaluating the Visual Word Sense Disambiguation (Visual-WSD) task in Ukrainian. The main goal of the Visual-WSD task is to identify, with minima…
DeVOS: Flow-Guided Deformable Transformer for Video Object Segmentation
Volodymyr Fedynyak, Yaroslav Romanus, Bohdan Hlovatskyi +4
The recent works on Video Object Segmentation achieved remarkable results by matching dense semantic and instance-level features between the current and previous frames for long-ti…
Global Motion Understanding in Large-Scale Video Object Segmentation
Volodymyr Fedynyak, Yaroslav Romanus, Oles Dobosevych +2
In this paper, we show that transferring knowledge from other domains of video understanding combined with large-scale learning can improve robustness of Video Object Segmentation…
Is Mapping Necessary for Realistic PointGoal Navigation?
Ruslan Partsey, Erik Wijmans, Naoki Yokoyama +3
Can an autonomous agent navigate in a new environment without building an explicit map? For the task of PointGoal navigation ('Go to , ') under idealized settings (no RGB-D…
OpenGlue: Open Source Graph Neural Net Based Pipeline for Image Matching
Ostap Viniavskyi, Mariia Dobko, Dmytro Mishkin +1
We present OpenGlue: a free open-source framework for image matching, that uses a Graph Neural Network-based matcher inspired by SuperGlue \cite{sarlin20superglue}. We show that in…
Towards realistic symmetry-based completion of previously unseen point clouds
Taras Rumezhak, Oles Dobosevych, Rostyslav Hryniv +3
3D scanning is a complex multistage process that generates a point cloud of an object typically containing damaged parts due to occlusions, reflections, shadows, scanner motion, sp…
Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration
Yaroslava Lochman, Oles Dobosevych, Rostyslav Hryniv +1
This paper proposes minimal solvers that use combinations of imaged translational symmetries and parallel scene lines to jointly estimate lens undistortion with either affine recti…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…
Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images
Ostap Viniavskyi, Mariia Dobko, Oles Dobosevych
Deep Convolutional Neural Networks have proven effective in solving the task of semantic segmentation. However, their efficiency heavily relies on the pixel-level annotations that…
NoPeopleAllowed: The Three-Step Approach to Weakly Supervised Semantic Segmentation
Mariia Dobko, Ostap Viniavskyi, Oles Dobosevych
We propose a novel approach to weakly supervised semantic segmentation, which consists of three consecutive steps. The first two steps extract high-quality pseudo masks from image-…