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Adam Pardyl

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3 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

FlySearch: Exploring how vision-language models explore

Adam Pardyl
Dominik Matuszek
Mateusz Przebieracz
Marek Cygan
Bartosz Zieliński
Maciej Wolczyk

The real world is messy and unstructured. Uncovering critical information often requires active, goal-driven exploration. It remains to be seen whether Vision-Language Models (VLMs), which recently emerged as a popular zero-shot tool in many difficult tasks, can operate effectively in such conditions. In this paper, we answer this question by introducing FlySearch, a 3D, outdoor, photorealistic environment for searching and navigating to objects in complex scenes. We define three sets of scenarios with varying difficulty and observe that state-of-the-art VLMs cannot reliably solve even the simplest exploration tasks, with the gap to human performance increasing as the tasks get harder. We identify a set of central causes, ranging from vision hallucination, through context misunderstanding, to task planning failures, and we show that some of them can be addressed by finetuning. We publicly release the benchmark, scenarios, and the underlying codebase.

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IJCAI Conference 2023 Conference Paper

Active Visual Exploration Based on Attention-Map Entropy

Adam Pardyl
Grzegorz Rypeść
Grzegorz Kurzejamski
Bartosz Zieliński
Tomasz Trzciński

Active visual exploration addresses the issue of limited sensor capabilities in real-world scenarios, where successive observations are actively chosen based on the environment. To tackle this problem, we introduce a new technique called Attention-Map Entropy (AME). It leverages the internal uncertainty of the transformer-based model to determine the most informative observations. In contrast to existing solutions, it does not require additional loss components, which simplifies the training. Through experiments, which also mimic retina-like sensors, we show that such simplified training significantly improves the performance of reconstruction, segmentation and classification on publicly available datasets.

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ECAI Conference 2023 Conference Paper

CompLung: Comprehensive Computer-Aided Diagnosis of Lung Cancer

Adam Pardyl
Dawid Damian Rymarczyk
Joanna Jaworek-Korjakowska
Dariusz Kucharski
Andrzej Brodzicki
Julia Lasek
Zofia Schneider
Iwona Kucybala

Lung cancer is a leading cause of cancer-related deaths, and early diagnosis is crucial for its effective treatment. That is why computer-aided tools have been developed to support particular steps of CT scan analysis, including lung segmentation, suspicious region detection, and patient-level diagnosis. However, none of the previous approaches addressed this process comprehensively. To fill this gap, we introduce CompLung, a comprehensive tool for lung cancer diagnosis that performs all of the above-listed steps in an end-to-end manner. We have trained the CompLung architecture using the publicly available LIDC-IDRI dataset extended with lung segmentation masks obtained from our internal radiologists, which we make publicly available to boost the research on this emerging topic. Finally, we conduct extensive experiments and demonstrate the superior performance and interpretability of CompLung compared to existing methods for lung cancer diagnosis.

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