6 citations · 7 across the 7 of their papers we have counts for
9 papers
DisParQ: Self-Supervised Part Concepts for Interpretable Vision Foundation Models
Adam Pardyl, Siddhartha Gairola, Sukrut Rao +4
Concept-based vision models represent images through an intermediate layer of human-inspectable concepts, so what a model relies on can be traced to those concepts. However, those…
SpaRRTa: A Synthetic Benchmark for Evaluating Spatial Intelligence in Visual Foundation Models
Turhan Can Kargin, Wojciech Jasiński, Adam Pardyl +2
Visual Foundation Models (VFMs), such as DINO and CLIP, excel in semantic understanding of images but exhibit limited spatial reasoning capabilities, which limits their applicabili…
FlySearch: Exploring how vision-language models explore
Adam Pardyl, Dominik Matuszek, Mateusz Przebieracz +3
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…
AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in Blood Smears of Septic Patients
Agnieszka Sroka-Oleksiak, Adam Pardyl, Dawid Rymarczyk +10
Sepsis is a life-threatening condition which requires rapid diagnosis and treatment. Traditional microbiological methods are time-consuming and expensive. In response to these chal…
AdaGlimpse: Active Visual Exploration with Arbitrary Glimpse Position and Scale
Adam Pardyl, Michał Wronka, Maciej Wołczyk +3
Active Visual Exploration (AVE) is a task that involves dynamically selecting observations (glimpses), which is critical to facilitate comprehension and navigation within an enviro…
ProPML: Probability Partial Multi-label Learning
Łukasz Struski, Adam Pardyl, Jacek Tabor +1
Partial Multi-label Learning (PML) is a type of weakly supervised learning where each training instance corresponds to a set of candidate labels, among which only some are true. In…
Beyond Grids: Exploring Elastic Input Sampling for Vision Transformers
Adam Pardyl, Grzegorz Kurzejamski, Jan Olszewski +2
Vision transformers have excelled in various computer vision tasks but mostly rely on rigid input sampling using a fixed-size grid of patches. It limits their applicability in real…
Active Visual Exploration Based on Attention-Map Entropy
Adam Pardyl, Grzegorz Rypeść, Grzegorz Kurzejamski +2
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…
ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification
Dawid Rymarczyk, Adam Pardyl, Jarosław Kraus +3
Multiple Instance Learning (MIL) gains popularity in many real-life machine learning applications due to its weakly supervised nature. However, the corresponding effort on explaini…