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20192026
most citedMixMatch Domain Adaptaion: Prize-winning solution for both tracks of VisDA 2019 challenge

11 citations · 21 across the 13 of their papers we have counts for

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17 papers · 1 filter

cs.CV2026

Enabling Training-Free Text-Based Remote Sensing Segmentation

Jose Sosa, Danila Rukhovich, Anis Kacem +1

Recent advances in Vision Language Models (VLMs) and Vision Foundation Models (VFMs) have opened new opportunities for zero-shot text-guided segmentation of remote sensing imagery.…

cs.CV2026

Z3D: Zero-Shot 3D Visual Grounding from Images

Nikita Drozdov, Andrey Lemeshko, Nikita Gavrilov +3

3D visual grounding (3DVG) aims to localize objects in a 3D scene based on natural language queries. In this work, we explore zero-shot 3DVG from multi-view images alone, without r…

cs.CV2025

Zoo3D: Zero-Shot 3D Object Detection at Scene Level

Andrey Lemeshko, Bulat Gabdullin, Nikita Drozdov +3

3D object detection is fundamental for spatial understanding. Real-world environments demand models capable of recognizing diverse, previously unseen objects, which remains a major…

cs.CV2025

MiCADangelo: Fine-Grained Reconstruction of Constrained CAD Models from 3D Scans

Ahmet Serdar Karadeniz, Dimitrios Mallis, Danila Rukhovich +3

Computer-Aided Design (CAD) plays a foundational role in modern manufacturing and product development, often requiring designers to modify or build upon existing models. Converting…

cs.CV2025

TUN3D: Towards Real-World Scene Understanding from Unposed Images

Anton Konushin, Nikita Drozdov, Bulat Gabdullin +4

Layout estimation and 3D object detection are two fundamental tasks in indoor scene understanding. When combined, they enable the creation of a compact yet semantically rich spatia…

cs.CV2025

MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks

Jose Sosa, Danila Rukhovich, Anis Kacem +1

Multi-modal data in Earth Observation (EO) presents a huge opportunity for improving transfer learning capabilities when pre-training deep learning models. Unlike prior work that o…