Bartosz Zieliński
14 papers · 2018–2025 · 6 conferences · across top CS/AI conferences
Achievements
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🌉 Interdisciplinary Bridge 🏃 Academic Marathon (7) 🌍 Conference Polyglot (6) 🌈 Renaissance Researcher (6) 🗺️ Taxonomy Completionist (20)
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Renaissance Researcher
(6)
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Interdisciplinary Bridge
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Unstoppable
(5)
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Keyword Collector
(55)
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Century Club
(14)
Conferences
WACV (5)
IJCAI (3)
ECCV (2)
ICCV (2)
AAAI (1)
NIPS (1)
Top co-authors
Keywords
active visual exploration
(3)
prototypical part
(2)
representation learning
(2)
self-supervised learning
(2)
vision transformer
(2)
feature learning
(1)
weakly supervised learning
(1)
image classification
(1)
prototype learning
(1)
semantic segmentation
(1)
computer vision
(1)
model architecture
(1)
explainable ai
(1)
visual representation
(1)
computational efficiency
(1)
topological data analysis
(1)
multiple instance learning
(1)
visual reconstruction
(1)
image representation
(1)
feature extraction
(1)
Papers
TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration
WACV 2025
Beyond [cls]: Exploring the True Potential of Masked Image Modeling Representations
ICCV 2025
Beyond Grids: Exploring Elastic Input Sampling for Vision Transformers
WACV 2025
Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations
AAAI 2024
AdaGlimpse: Active Visual Exploration with Arbitrary Glimpse Position and Scale
ECCV 2024
ICICLE: Interpretable Class Incremental Continual Learning
ICCV 2023
SONGs: Self-Organizing Neural Graphs
WACV 2023
Active Visual Exploration Based on Attention-Map Entropy
IJCAI 2023
ProtoSeg: Interpretable Semantic Segmentation With Prototypical Parts
WACV 2023
Interpretable Image Classification with Differentiable Prototypes Assignment
ECCV 2022
Explaining Self-Supervised Image Representations with Visual Probing
IJCAI 2021
Kernel Self-Attention for Weakly-Supervised Image Classification Using Deep Multiple Instance Learning
WACV 2021
Persistence Bag-of-Words for Topological Data Analysis
IJCAI 2019
Processing of missing data by neural networks
NIPS 2018