Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Birdal, Tolga"'
Autor:
Vutukur, Shishir Reddy, Haugaard, Rasmus Laurvig, Huang, Junwen, Busam, Benjamin, Birdal, Tolga
Object pose distribution estimation is crucial in robotics for better path planning and handling of symmetric objects. Recent distribution estimation approaches employ contrastive learning-based approaches by maximizing the likelihood of a single pos
Externí odkaz:
http://arxiv.org/abs/2409.06683
Seams, distortions, wasted UV space, vertex-duplication, and varying resolution over the surface are the most prominent issues of the standard UV-based texturing of meshes. These issues are particularly acute when automatic UV-unwrapping techniques a
Externí odkaz:
http://arxiv.org/abs/2408.16762
We present a novel set of rigorous and computationally efficient topology-based complexity notions that exhibit a strong correlation with the generalization gap in modern deep neural networks (DNNs). DNNs show remarkable generalization properties, ye
Externí odkaz:
http://arxiv.org/abs/2407.08723
Autor:
Vutukur, Shishir Reddy, Brock, Heike, Busam, Benjamin, Birdal, Tolga, Hutter, Andreas, Ilic, Slobodan
Publikováno v:
3DV 2024
Object Pose Estimation is a crucial component in robotic grasping and augmented reality. Learning based approaches typically require training data from a highly accurate CAD model or labeled training data acquired using a complex setup. We address th
Externí odkaz:
http://arxiv.org/abs/2406.13796
Autor:
Ballester, Rubén, Hernández-García, Pablo, Papillon, Mathilde, Battiloro, Claudio, Miolane, Nina, Birdal, Tolga, Casacuberta, Carles, Escalera, Sergio, Hajij, Mustafa
Topological Deep Learning seeks to enhance the predictive performance of neural network models by harnessing topological structures in input data. Topological neural networks operate on spaces such as cell complexes and hypergraphs, that can be seen
Externí odkaz:
http://arxiv.org/abs/2405.14094
Faithfully modeling the space of articulations is a crucial task that allows recovery and generation of realistic poses, and remains a notorious challenge. To this end, we introduce Neural Riemannian Distance Fields (NRDFs), data-driven priors modeli
Externí odkaz:
http://arxiv.org/abs/2403.03122
Publikováno v:
IEEE/CVF conference on computer vision and pattern recognition 2024
3D shape generation from text is a fundamental task in 3D representation learning. The text-shape pairs exhibit a hierarchical structure, where a general text like ``chair" covers all 3D shapes of the chair, while more detailed prompts refer to more
Externí odkaz:
http://arxiv.org/abs/2403.00372
Autor:
Papamarkou, Theodore, Birdal, Tolga, Bronstein, Michael, Carlsson, Gunnar, Curry, Justin, Gao, Yue, Hajij, Mustafa, Kwitt, Roland, Liò, Pietro, Di Lorenzo, Paolo, Maroulas, Vasileios, Miolane, Nina, Nasrin, Farzana, Ramamurthy, Karthikeyan Natesan, Rieck, Bastian, Scardapane, Simone, Schaub, Michael T., Veličković, Petar, Wang, Bei, Wang, Yusu, Wei, Guo-Wei, Zamzmi, Ghada
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation
Externí odkaz:
http://arxiv.org/abs/2402.08871
Autor:
Hajij, Mustafa, Papillon, Mathilde, Frantzen, Florian, Agerberg, Jens, AlJabea, Ibrahem, Ballester, Ruben, Battiloro, Claudio, Bernárdez, Guillermo, Birdal, Tolga, Brent, Aiden, Chin, Peter, Escalera, Sergio, Fiorellino, Simone, Gardaa, Odin Hoff, Gopalakrishnan, Gurusankar, Govil, Devendra, Hoppe, Josef, Karri, Maneel Reddy, Khouja, Jude, Lecha, Manuel, Livesay, Neal, Meißner, Jan, Mukherjee, Soham, Nikitin, Alexander, Papamarkou, Theodore, Prílepok, Jaro, Ramamurthy, Karthikeyan Natesan, Rosen, Paul, Guzmán-Sáenz, Aldo, Salatiello, Alessandro, Samaga, Shreyas N., Scardapane, Simone, Schaub, Michael T., Scofano, Luca, Spinelli, Indro, Telyatnikov, Lev, Truong, Quang, Walters, Robin, Yang, Maosheng, Zaghen, Olga, Zamzmi, Ghada, Zia, Ali, Miolane, Nina
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. To
Externí odkaz:
http://arxiv.org/abs/2402.02441
Principal component analysis (PCA), along with its extensions to manifolds and outlier contaminated data, have been indispensable in computer vision and machine learning. In this work, we present a unifying formalism for PCA and its variants, and int
Externí odkaz:
http://arxiv.org/abs/2401.04071