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Tomasz Trzciński

6 accepted papers

2024

CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation

Monika Wysoczańska*, Oriane Siméoni, Michaël Ramamonjisoa, Andrei Bursuc, Tomasz Trzciński, Patrick Pérez
ECCV 2024poster

"The popular CLIP model displays impressive zero-shot capabilities thanks to its seamless interaction with arbitrary text prompts. However, its lack of spatial awareness makes it unsuitable for dense computer vision tasks, e.g., semantic segmentation, without an additional fine-tuning step that ofte…

Cited by 31Source ↗PDF ↗code ↗Scholar ↗
2024

HyperCube: Implicit Field Representations of Voxelized 3D Models (Student Abstract)

Magdalena Proszewska, Marcin Mazur, Tomasz Trzciński, Przemysław Spurek
AAAI 2024technical

Implicit field representations offer an effective way of generating 3D object shapes. They leverage an implicit decoder (IM-NET) trained to take a 3D point coordinate concatenated with a shape encoding and to output a value indicating whether the point is outside the shape. This approach enables the…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2023

Active Visual Exploration Based on Attention-Map Entropy

Adam Pardyl, Grzegorz Rypeść, Grzegorz Kurzejamski, Bartosz Zieliński, Tomasz Trzciński
IJCAI 2023poster

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 tackle this problem, we introduce a new technique called Attention-Map Entropy (AME). It leverages the internal uncerta…

Cited by 8Source ↗PDF ↗code ↗Scholar ↗
2023

BlendFields: Few-Shot Example-Driven Facial Modeling

Kacper Kania, Stephan J. Garbin, Andrea Tagliasacchi, Virginia Estellers, Kwang Moo Yi, Julien Valentin +2
CVPR 2023poster

Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven, requiring an extensive corpus of data not publicly accessible to the research community, or fail to capture fine detai…

Cited by 8Source ↗PDF ↗Scholar ↗
2022

CoNeRF: Controllable Neural Radiance Fields

Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzciński, Andrea Tagliasacchi
CVPR 2022poster

We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i.e. camera control). We allow the user to annotate which part of the scene one wishes to control with just a small number of mask annotations in the training images. Our key idea i…

Cited by 109PDF ↗code ↗Scholar ↗
2022

Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning

Kamil Deja, Paweł Wawrzyński, Wojciech Masarczyk, Daniel Marczak, Tomasz Trzciński
IJCAI 2022poster

We propose a new method for unsupervised generative continual learning through realignment of Variational Autoencoder's latent space. Deep generative models suffer from catastrophic forgetting in the same way as other neural structures. Recent generative continual learning works approach this proble…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗