activity
20202024
most citedMineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

6 citations · 9 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2024

Semi-Supervised Fine-Tuning of Vision Foundation Models with Content-Style Decomposition

Mariia Drozdova, Vitaliy Kinakh, Yury Belousov +2

In this paper, we present a semi-supervised fine-tuning approach designed to improve the performance of pre-trained foundation models on downstream tasks with limited labeled data.…

cs.CV2024

Evaluation of Security of ML-based Watermarking: Copy and Removal Attacks

Vitaliy Kinakh, Brian Pulfer, Yury Belousov +3

The vast amounts of digital content captured from the real world or AI-generated media necessitate methods for copyright protection, traceability, or data provenance verification.…

cs.CV20223 cited

Solving the Weather4cast Challenge via Visual Transformers for 3D Images

Yury Belousov, Sergey Polezhaev, Brian Pulfer

Accurately forecasting the weather is an important task, as many real-world processes and decisions depend on future meteorological conditions. The NeurIPS 2022 challenge entitled…

cs.CV2022

Digital twins of physical printing-imaging channel

Yury Belousov, Brian Pulfer, Roman Chaban +4

In this paper, we address the problem of modeling a printing-imaging channel built on a machine learning approach a.k.a. digital twin for anti-counterfeiting applications based on…

cs.CR2022

Printing variability of copy detection patterns

Roman Chaban, Olga Taran, Joakim Tutt +4

Copy detection pattern (CDP) is a novel solution for products' protection against counterfeiting, which gains its popularity in recent years. CDP attracts the anti-counterfeiting i…

cs.CV2022

Anomaly localization for copy detection patterns through print estimations

Brian Pulfer, Yury Belousov, Joakim Tutt +4

Copy detection patterns (CDP) are recent technologies for protecting products from counterfeiting. However, in contrast to traditional copy fakes, deep learning-based fakes have sh…