1 code implementation • 6 Aug 2024 • Joanna Kaleta, Kacper Kania, Tomasz Trzcinski, Marek Kowalski
Our approach yields high-quality scene reconstructions and enables realistic lighting synthesis under novel environment maps.
no code implementations • 21 Dec 2023 • Michal K. Grzeszczyk, Tadeusz Satlawa, Angela Lungu, Andrew Swift, Andrew Narracott, Rod Hose, Tomasz Trzcinski, Arkadiusz Sitek
We show using the ablation study, that physics-informed feature engineering based on models of blood circulation increases the performance of Gradient Boosting Decision Trees-based algorithms for classification of PH and regression of values of mPAP.
1 code implementation • 31 Jan 2023 • Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak
Joint machine learning models that allow synthesizing and classifying data often offer uneven performance between those tasks or are unstable to train.
no code implementations • 30 May 2022 • Anna Wroblewska, Jozef Jasek, Bogdan Jastrzebski, Stanislaw Pawlak, Anna Grzywacz, Cheong Siew Ann, Tan Seng Chee, Tomasz Trzcinski, Janusz Holyst
Artificial Intelligence in higher education opens new possibilities for improving the lecturing process, such as enriching didactic materials, helping in assessing students' works or even providing directions to the teachers on how to enhance the lectures.
1 code implementation • 24 Oct 2021 • Jacek Komorowski, Monika Wysoczanska, Tomasz Trzcinski
The paper presents a deep neural network-based method for global and local descriptors extraction from a point cloud acquired by a rotating 3D LiDAR.
1 code implementation • 6 Oct 2021 • Jacek Komorowski, Monika Wysoczanska, Tomasz Trzcinski
In this work, we propose a method for large-scale topological localization based on radar scan images using learned descriptors.
1 code implementation • 12 Apr 2021 • Jacek Komorowski, Monika Wysoczanska, Tomasz Trzcinski
We also identify dominating modality problem when training a multimodal descriptor.
no code implementations • 17 Feb 2021 • Ivona Tautkute, Tomasz Trzcinski
This paper addresses the problem of media retrieval using a multimodal query (a query which combines visual input with additional semantic information in natural language feedback).
no code implementations • 30 Nov 2020 • Maciej Zięba, Marcin Przewięźlikowski, Marek Śmieja, Jacek Tabor, Tomasz Trzcinski, Przemysław Spurek
Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenarios, such as modeling behavior of humans.
no code implementations • 10 Dec 2019 • Grzegorz Kurzejamski, Jacek Komorowski, Lukasz Dabala, Konrad Czarnota, Simon Lynen, Tomasz Trzcinski
In this paper, we present a framework for computing dense keypoint correspondences between images under strong scene appearance changes.
no code implementations • 11 Sep 2019 • Norbert Kapinski, Jedrzej M. Nowosielski, Maciej E. Marchwiany, Jakub Zielinski, Beata Ciszkowska-Lyson, Bartosz A. Borucki, Tomasz Trzcinski, Krzysztof S. Nowinski
In comparison with the previous baseline method, our approach significantly improves correlation in all of the six parameters assessed.
no code implementations • 11 Sep 2019 • Piotr Woznicki, Przemyslaw Przybyszewski, Norbert Kapinski, Jakub Zielinski, Beata Ciszkowska-Lyson, Bartosz A. Borucki, Tomasz Trzcinski, Krzysztof S. Nowinski
The obtained estimates show a high correlation with the assessment of expert radiologists, with respect to all key parameters describing healing progress.
1 code implementation • 2 Jan 2019 • Michal Koperski, Tomasz Konopczynski, Rafał Nowak, Piotr Semberecki, Tomasz Trzcinski
In this paper, we propose a novel method to incorporate partial evidence in the inference of deep convolutional neural networks.
Ranked #1 on
Semantic Segmentation
on PASCAL VOC 2011 test
2 code implementations • 23 Oct 2018 • Ivona Tautkute, Tomasz Trzcinski
Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life.
no code implementations • 28 Sep 2018 • Jacek Komorowski, Tomasz Trzcinski
In this paper we present an efficient method for aggregating binary feature descriptors to allow compact representation of 3D scene model in incremental structure-from-motion and SLAM applications.
no code implementations • 28 Sep 2018 • Tomasz Trzcinski, Jacek Komorowski, Lukasz Dabala, Konrad Czarnota, Grzegorz Kurzejamski, Simon Lynen
Numerous computer vision applications rely on local feature descriptors, such as SIFT, SURF or FREAK, for image matching.
no code implementations • 28 Sep 2018 • Jacek Komorowski, Konrad Czarnota, Tomasz Trzcinski, Lukasz Dabala, Simon Lynen
In the recent years, a number of novel, deep-learning based, interest point detectors, such as LIFT, DELF, Superpoint or LF-Net was proposed.
1 code implementation • NeurIPS 2018 • Maciej Zieba, Piotr Semberecki, Tarek El-Gaaly, Tomasz Trzcinski
In this paper, we propose a novel regularization method for Generative Adversarial Networks, which allows the model to learn discriminative yet compact binary representations of image patches (image descriptors).
no code implementations • 13 Jun 2018 • Norbert Kapinski, Jakub Zielinski, Bartosz A. Borucki, Tomasz Trzcinski, Beata Ciszkowska-Lyson, Krzysztof S. Nowinski
Quantitative assessment of a treatment progress in the Achilles tendon healing process - one of the most common musculoskeletal disorder in modern medical practice - is typically a long and complex process: multiple MRI protocols need to be acquired and analysed by radiology experts.
no code implementations • 27 Apr 2018 • Adam Słucki, Tomasz Trzcinski, Adam Bielski, Paweł Cyrta
The main component of our system, i. e. the text recognition module, is inspired by a convolutional recurrent neural network architecture and we improve its performance using synthetically generated dataset of over 600, 000 images with text prepared by authors specifically for this task.
no code implementations • 26 Apr 2018 • Adam Bielski, Tomasz Trzcinski
Predicting popularity of social media videos before they are published is a challenging task, mainly due to the complexity of content distribution network as well as the number of factors that play part in this process.
no code implementations • 23 Apr 2018 • Witold Oleszkiewicz, Peter Kairouz, Karol Piczak, Ram Rajagopal, Tomasz Trzcinski
Extensive evaluation on a biometric dataset of fingerprints and cartoon faces confirms usefulness of our simple yet effective method.
no code implementations • 22 Apr 2018 • Ivona Tautkute, Tomasz Trzcinski, Adam Bielski
Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life.
no code implementations • 25 Jan 2018 • Tomasz Trzcinski, Adam Bielski, Paweł Cyrta, Matthew Zak
In this work, we present a comprehensive overview of machine learning-empowered tools we developed for video creators at Group Nine Media - one of the major social media companies that creates short-form videos with over three billion views per month.
no code implementations • 8 Jan 2018 • Ivona Tautkute, Tomasz Trzcinski, Aleksander Skorupa, Lukasz Brocki, Krzysztof Marasek
In this paper, we propose a multimodal search engine that combines visual and textual cues to retrieve items from a multimedia database aesthetically similar to the query.
no code implementations • 9 Aug 2017 • Michal Komorowski, Tomasz Trzcinski
Approximate nearest neighbour (ANN) search is one of the most important problems in computer science fields such as data mining or computer vision.
no code implementations • 27 Jul 2017 • Maciej Suchecki, Tomasz Trzcinski
Evaluating aesthetic value of digital photographs is a challenging task, mainly due to numerous factors that need to be taken into account and subjective manner of this process.
no code implementations • 21 Jul 2017 • Wociech Stokowiec, Tomasz Trzcinski, Krzysztof Wolk, Krzysztof Marasek, Przemyslaw Rokita
To take advantage of this phenomenon, we propose a new method based on a bidirectional Long Short-Term Memory (LSTM) neural network designed to predict the popularity of online content using only its title.
no code implementations • 21 Jul 2017 • Tomasz Trzcinski, Pawel Andruszkiewicz, Tomasz Bochenski, Przemyslaw Rokita
In this paper, we address the problem of popularity prediction of online videos shared in social media.
no code implementations • 21 Jul 2017 • Jacek Komorowski, Tomasz Trzcinski
In this paper we evaluate performance of data-dependent hashing methods on binary data.
3 code implementations • 6 Jun 2017 • Marek Kowalski, Jacek Naruniec, Tomasz Trzcinski
Our method uses entire face images at all stages, contrary to the recently proposed face alignment methods that rely on local patches.
Ranked #4 on
Face Alignment
on 300W Split 2
(NME (inter-ocular) metric)
no code implementations • 21 Oct 2015 • Tomasz Trzcinski, Przemyslaw Rokita
In this work, we propose a regression method to predict the popularity of an online video based on temporal and visual cues.
no code implementations • CVPR 2013 • Tomasz Trzcinski, Mario Christoudias, Pascal Fua, Vincent Lepetit
Binary keypoint descriptors provide an efficient alternative to their floating-point competitors as they enable faster processing while requiring less memory.
no code implementations • NeurIPS 2012 • Tomasz Trzcinski, Mario Christoudias, Vincent Lepetit, Pascal Fua
The main goal of local feature descriptors is to distinctively represent a salient image region while remaining invariant to viewpoint and illumination changes.