Search Results for author: Maciej Zięba

Found 24 papers, 17 papers with code

Dimma: Semi-supervised Low Light Image Enhancement with Adaptive Dimming

1 code implementation14 Oct 2023 Wojciech Kozłowski, Michał Szachniewicz, Michał Stypułkowski, Maciej Zięba

To fill this gap, we propose Dimma, a semi-supervised approach that aligns with any camera by utilizing a small set of image pairs to replicate scenes captured under extreme lighting conditions taken by that specific camera.

Low-Light Image Enhancement

Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks

1 code implementation13 Jul 2023 Mateusz Baran, Joanna Baran, Mateusz Wójcik, Maciej Zięba, Adam Gonczarek

This highlights the need for future work to develop more effective OOD detection approaches for the NLP problems, and our work provides a well-defined foundation for further research in this area.

Out-of-Distribution Detection text-classification +1

MultiPlaneNeRF: Neural Radiance Field with Non-Trainable Representation

1 code implementation17 May 2023 Dominik Zimny, Jacek Tabor, Maciej Zięba, Przemysław Spurek

Consequently, we can only replace the 2D images (without additional training) to produce a NeRF representation of the new object.

NeRFlame: FLAME-based conditioning of NeRF for 3D face rendering

1 code implementation10 Mar 2023 Wojciech Zając, Joanna Waczyńska, Piotr Borycki, Jacek Tabor, Maciej Zięba, Przemysław Spurek

In contrast to traditional NeRF-based structures that use neural networks for RGB color and volume density modeling, our approach utilizes the FLAME mesh as a distinct density volume.

HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN

1 code implementation27 Jan 2023 Adam Kania, Artur Kasymov, Maciej Zięba, Przemysław Spurek

Our architecture produces 2D images, but we use 3D-aware NeRF representation, which forces the model to produce correct 3D objects.

Speech Driven Video Editing via an Audio-Conditioned Diffusion Model

no code implementations10 Jan 2023 Dan Bigioi, Shubhajit Basak, Michał Stypułkowski, Maciej Zięba, Hugh Jordan, Rachel McDonnell, Peter Corcoran

Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diffusion model.

Denoising Face Model +2

Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation

no code implementations6 Jan 2023 Michał Stypułkowski, Konstantinos Vougioukas, Sen He, Maciej Zięba, Stavros Petridis, Maja Pantic

Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos.

Talking Face Generation Video Generation

TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression

no code implementations8 Jun 2022 Patryk Wielopolski, Maciej Zięba

The tree-based ensembles are known for their outstanding performance in classification and regression problems characterized by feature vectors represented by mixed-type variables from various ranges and domains.


Continual learning on 3D point clouds with random compressed rehearsal

no code implementations16 May 2022 Maciej Zamorski, Michał Stypułkowski, Konrad Karanowski, Tomasz Trzciński, Maciej Zięba

By using rehearsal and reconstruction as regularization methods of the learning process, our approach achieves a significant decrease of catastrophic forgetting compared to the existing solutions on several most popular point cloud datasets considering two continual learning settings: when a task is known beforehand, and in the challenging scenario of when task information is unknown to the model.

Continual Learning Visual Reasoning

Flow Plugin Network for conditional generation

1 code implementation7 Oct 2021 Patryk Wielopolski, Michał Koperski, Maciej Zięba

Generative models have gained many researchers' attention in the last years resulting in models such as StyleGAN for human face generation or PointFlow for the 3D point cloud generation.

Conditional Image Generation Face Generation +2

PluGeN: Multi-Label Conditional Generation From Pre-Trained Models

1 code implementation18 Sep 2021 Maciej Wołczyk, Magdalena Proszewska, Łukasz Maziarka, Maciej Zięba, Patryk Wielopolski, Rafał Kurczab, Marek Śmieja

Modern generative models achieve excellent quality in a variety of tasks including image or text generation and chemical molecule modeling.

Text Generation

RegFlow: Probabilistic Flow-based Regression for Future Prediction

no code implementations30 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.

Future prediction regression

Representing Point Clouds with Generative Conditional Invertible Flow Networks

1 code implementation7 Oct 2020 Michał Stypułkowski, Kacper Kania, Maciej Zamorski, Maciej Zięba, Tomasz Trzciński, Jan Chorowski

To exploit similarities between same-class objects and to improve model performance, we turn to weight sharing: networks that model densities of points belonging to objects in the same family share all parameters with the exception of a small, object-specific embedding vector.

Point Cloud Registration

UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Tree

1 code implementation16 Jun 2020 Kacper Kania, Maciej Zięba, Tomasz Kajdanowicz

On the contrary, we propose a model that extracts a CSG parse tree without any supervision - UCSG-Net.

3D Shape Reconstruction

HyperFlow: Representing 3D Objects as Surfaces

1 code implementation15 Jun 2020 Przemysław Spurek, Maciej Zięba, Jacek Tabor, Tomasz Trzciński

To that end, we devise a generative model that uses a hypernetwork to return the weights of a Continuous Normalizing Flows (CNF) target network.

Autonomous Driving Quantization

Hypernetwork approach to generating point clouds

2 code implementations ICML 2020 Przemysław Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zięba, Tomasz Trzciński

The main idea of our HyperCloud method is to build a hyper network that returns weights of a particular neural network (target network) trained to map points from a uniform unit ball distribution into a 3D shape.

Generating 3D Point Clouds

Conditional Invertible Flow for Point Cloud Generation

2 code implementations16 Oct 2019 Michał Stypułkowski, Maciej Zamorski, Maciej Zięba, Jan Chorowski

This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models.

Point Cloud Generation

Generative Adversarial Networks: recent developments

no code implementations16 Mar 2019 Maciej Zamorski, Adrian Zdobylak, Maciej Zięba, Jerzy Świątek

In traditional generative modeling, good data representation is very often a base for a good machine learning model.

BIG-bench Machine Learning

Semi-supervised learning with Bidirectional GANs

no code implementations28 Nov 2018 Maciej Zamorski, Maciej Zięba

In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model (BiGAN) in a semi-supervised manner.

General Classification Image Retrieval +1

Adversarial Autoencoders for Compact Representations of 3D Point Clouds

4 code implementations19 Nov 2018 Maciej Zamorski, Maciej Zięba, Piotr Klukowski, Rafał Nowak, Karol Kurach, Wojciech Stokowiec, Tomasz Trzciński

Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds.

3D Object Retrieval Clustering +4

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