Search Results - "Trzciński, Tomasz"

Adversarial autoencoders for compact representations of 3D point clouds by Zamorski, Maciej, Zięba, Maciej, Klukowski, Piotr, Nowak, Rafał, Kurach, Karol, Stokowiec, Wojciech, Trzciński, Tomasz

“…Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds. In this work, we present a…”
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Points2NeRF: Generating Neural Radiance Fields from 3D point cloud by Zimny, Dominik, Waczyńska, Joanna, Trzciński, Tomasz, Spurek, Przemysław

“…Neural Radiance Fields (NeRFs) offers a state-of-the-art quality in synthesizing novel views of complex 3D scenes from a small subset of base images. For NeRFs…”
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Continual learning on 3D point clouds with random compressed rehearsal by Zamorski, Maciej, Stypułkowski, Michał, Karanowski, Konrad, Trzciński, Tomasz, Zięba, Maciej

“…Contemporary deep neural networks offer state-of-the-art results when applied to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds…”
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Efficient GPU implementation of randomized SVD and its applications by Struski, Łukasz, Morkisz, Paweł, Spurek, Przemysław, Bernabeu, Samuel Rodriguez, Trzciński, Tomasz

“…Matrix decompositions are ubiquitous in machine learning, including applications in dimensionality reduction, data compression and deep learning algorithms…”
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Deep learning fetal ultrasound video model match human observers in biometric measurements by Płotka, Szymon, Klasa, Adam, Lisowska, Aneta, Seliga-Siwecka, Joanna, Lipa, Michał, Trzciński, Tomasz, Sitek, Arkadiusz

“…This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head…”
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Representing point clouds with generative conditional invertible flow networks by Stypułkowski, Michał, Kania, Kacper, Zamorski, Maciej, Zięba, Maciej, Trzciński, Tomasz, Chorowski, Jan

“…•We propose a novel generative model for 3D point clouds.•The model utilizes two normalizing flows - one produces an object descriptor and conditions the other…”
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Synthetic Image Translation for Football Players Pose Estimation by Michał Sypetkowski, Grzegorz Sarwas, Tomasz Trzciński

“…In this paper, we present an approach for football players pose estimation on very low-resolution images. The camera recording the football match is far away…”
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CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free by Wysoczanska, Monika, Ramamonjisoa, Michael, Trzcinski, Tomasz, Simeoni, Oriane

“…The emergence of CLIP has opened the way for open-world image perception. The zero-shot classification capabilities of the model are impressive but are harder…”
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Zero time waste in pre-trained early exit neural networks by Wójcik, Bartosz, Przewiȩźlikowski, Marcin, Szatkowski, Filip, Wołczyk, Maciej, Bałazy, Klaudia, Krzepkowski, Bartłomiej, Podolak, Igor, Tabor, Jacek, Śmieja, Marek, Trzciński, Tomasz

“…The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods strive…”
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End-to-End Sinkhorn Autoencoder With Noise Generator by Deja, Kamil, Dubinski, Jan, Nowak, Piotr, Wenzel, Sandro, Spurek, Przemsysaw, Trzcinski, Tomasz

“…In this work, we propose a novel end-to-end Sinkhorn Autoencoder with a noise generator for efficient data collection simulation. Simulating processes that aim…”
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Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning by Szatkowski, Filip, Pyla, Mateusz, Przewiezlikowski, Marcin, Cygert, Sebastian, Twardowski, Bartlomiej, Trzcinski, Tomasz

“…In this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent…”
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HyperColor: A HyperNetwork Approach for Synthesizing Autocolored 3-D Models for Game Scenes Population by Kostiuk, Ivan, Stachura, Przemyslaw, Tadeja, Slawomir K., Trzcinski, Tomasz, Spurek, Przemyslaw

“…Designing a 3-D game scene is a tedious task that often requires a substantial amount of work. Typically, this task involves the synthesis and coloring of 3-D…”
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BlendFields: Few-Shot Example-Driven Facial Modeling by Kania, Kacper, Garbin, Stephan J., Tagliasacchi, Andrea, Estellers, Virginia, Yi, Kwang Moo, Valentin, Julien, Trzcinski, Tomasz, Kowalski, Marek

“…Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods…”
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Predicting Popularity of Online Videos Using Support Vector Regression by Trzcinski, Tomasz, Rokita, Przemyslaw

“…In this work, we propose a regression method to predict the popularity of an online video measured by its number of views. Our method uses Support Vector…”
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Monitoring of the Achilles tendon healing process: can artificial intelligence be helpful? by Kapiński, Norbert, Zieliński, Jakub, Borucki, Bartosz A, Trzciński, Tomasz, Ciszkowska-Łysoń, Beata, Zdanowicz, Urszula, Śmigielski, Robert, Nowiński, Krzysztof S

“…The aim of this study was to verify improved, ensemble-based strategy for inferencing with use of our solution for quantitative assessment of tendons and…”
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BRIEF: Computing a Local Binary Descriptor Very Fast by Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., Fua, P.

“…Binary descriptors are becoming increasingly popular as a means to compare feature points very fast while requiring comparatively small amounts of memory. The…”
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Understanding Multimodal Popularity Prediction of Social Media Videos With Self-Attention by Bielski, Adam, Trzcinski, Tomasz

“…Predicting popularity of social media videos before they are published is a challenging task, mainly due to the complexity of content distribution network as…”
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Looking through the past: better knowledge retention for generative replay in continual learning by Khan, Valeriya, Cygert, Sebastian, Twardowski, Bartlomiej, Trzcinski, Tomasz

“…In this work, we improve the generative replay in a continual learning setting. We notice that in VAE-based generative replay, the generated features are quite…”
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AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation by Sojka, Damian, Cygert, Sebastian, Twardowski, Bartlomiej, Trzcinski, Tomasz

“…Test-time adaptation is a promising research direction that allows the source model to adapt itself to changes in data distribution without any supervision…”
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Toward Unsupervised Visual Reasoning: Do Off-the-Shelf Features Know How to Reason? by Wysoczanska, Monika, Monnier, Tom, Trzcinski, Tomasz, Picard, David

“…Recent advances in visual representation learning allowed for the construction of a plethora of powerful features that are ready to use for numerous downstream…”
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