Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Wysoczanska, Monika"'
Autor:
Wysoczańska, Monika, Vobecky, Antonin, Cardiel, Amaia, Trzciński, Tomasz, Marlet, Renaud, Bursuc, Andrei, Siméoni, Oriane
Recent VLMs, pre-trained on large amounts of image-text pairs to align both modalities, have opened the way to open-vocabulary semantic segmentation. Given an arbitrary set of textual queries, image regions are assigned the closest query in feature s
Externí odkaz:
http://arxiv.org/abs/2407.05061
Autor:
Wysoczańska, Monika, Siméoni, Oriane, Ramamonjisoa, Michaël, Bursuc, Andrei, Trzciński, Tomasz, Pérez, Patrick
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,
Externí odkaz:
http://arxiv.org/abs/2312.12359
Autor:
Wysoczanska, Monika, Beladev, Moran, Assaraf, Karen Lastmann, Wang, Fengjun, Kleinfeld, Ofri, Amsalem, Gil, Boker, Hadas Harush
Image collection summarization techniques aim to present a compact representation of an image gallery through a carefully selected subset of images that captures its semantic content. When it comes to web content, however, the ideal selection can var
Externí odkaz:
http://arxiv.org/abs/2310.19743
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 to use for dense tasks such as image segmentation. Several methods have proposed different
Externí odkaz:
http://arxiv.org/abs/2309.14289
Recent advances in visual representation learning allowed to build an abundance of powerful off-the-shelf features that are ready-to-use for numerous downstream tasks. This work aims to assess how well these features preserve information about the ob
Externí odkaz:
http://arxiv.org/abs/2212.10292
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. The descriptors can be used for two-stage 6DoF relocalization. First, a course position is retrieved
Externí odkaz:
http://arxiv.org/abs/2110.12486
In this work, we propose a method for large-scale topological localization based on radar scan images using learned descriptors. We present a simple yet efficient deep network architecture to compute a rotationally invariant discriminative global des
Externí odkaz:
http://arxiv.org/abs/2110.03081
We introduce a discriminative multimodal descriptor based on a pair of sensor readings: a point cloud from a LiDAR and an image from an RGB camera. Our descriptor, named MinkLoc++, can be used for place recognition, re-localization and loop closure p
Externí odkaz:
http://arxiv.org/abs/2104.05327
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.