5 citations · 7 across the 4 of their papers we have counts for
5 papers
Delineate Anything Flow: Fast, Country-Level Field Boundary Detection from Any Source
Mykola Lavreniuk, Nataliia Kussul, Andrii Shelestov +4
Accurate delineation of agricultural field boundaries from satellite imagery is essential for land management and crop monitoring, yet existing methods often produce incomplete bou…
Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery
Mykola Lavreniuk, Nataliia Kussul, Andrii Shelestov +4
The accurate delineation of agricultural field boundaries from satellite imagery is vital for land management and crop monitoring. However, current methods face challenges due to l…
Amodal Depth Anything: Amodal Depth Estimation in the Wild
Zhenyu Li, Mykola Lavreniuk, Jian Shi +2
Amodal depth estimation aims to predict the depth of occluded (invisible) parts of objects in a scene. This task addresses the question of whether models can effectively perceive t…
SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation
Mykola Lavreniuk
Self-supervised monocular depth estimation has garnered considerable attention for its applications in autonomous driving and robotics. While recent methods have made strides in le…
EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment
Mykola Lavreniuk, Shariq Farooq Bhat, Matthias Müller +1
This work presents the network architecture EVP (Enhanced Visual Perception). EVP builds on the previous work VPD which paved the way to use the Stable Diffusion network for comput…