activity
20242026
most citedThe Hidden Dangers of Browsing AI Agents

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Efficient Search of Implantable Adaptive Cells for Medical Image Segmentation

Emil Benedykciuk, Marcin Denkowski, Grzegorz M. Wójcik

Purpose: Adaptive skip modules can improve medical image segmentation, but searching for them is computationally costly. Implantable Adaptive Cells (IACs) are compact NAS modules i…

cs.CV2026

Prints in the Magnetic Dust: Robust Similarity Search in Legacy Media Images Using Checksum Count Vectors

Maciej Grzeszczuk, Kinga Skorupska, Grzegorz M. Wójcik

Digitizing magnetic media containing computer data is only the first step towards the preservation of early home computing era artifacts. The audio tape images must be decoded, ver…

cs.HC2025

Towards Effective Human Performance in XR Space Framework based on Real-time Eye Tracking Biofeedback

Barbara Karpowicz, Tomasz Kowalewski, Pavlo Zinevych +3

This paper proposes an eye tracking module for the XR Space Framework aimed at enhancing human performance in XR-based applications, specifically in training, screening, and teleop…

cs.CR2025★ 1 cited

The Hidden Dangers of Browsing AI Agents

Mykyta Mudryi, Markiyan Chaklosh, Grzegorz Wójcik

Autonomous browsing agents powered by large language models (LLMs) are increasingly used to automate web-based tasks. However, their reliance on dynamic content, tool execution, an…

cs.CV2024

Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation

Emil Benedykciuk, Marcin Denkowski, Grzegorz Wójcik

This paper introduces a novel approach to enhance the performance of pre-trained neural networks in medical image segmentation using gradient-based Neural Architecture Search (NAS)…