Zobrazeno 1 - 10
of 3 175
pro vyhledávání: '"Melo, A. M."'
Autor:
Rivera, Corban, Byrd, Grayson, Paul, William, Feldman, Tyler, Booker, Meghan, Holmes, Emma, Handelman, David, Kemp, Bethany, Badger, Andrew, Schmidt, Aurora, Jatavallabhula, Krishna Murthy, de Melo, Celso M, Seenivasan, Lalithkumar, Unberath, Mathias, Chellappa, Rama
Robotic planning and execution in open-world environments is a complex problem due to the vast state spaces and high variability of task embodiment. Recent advances in perception algorithms, combined with Large Language Models (LLMs) for planning, of
Externí odkaz:
http://arxiv.org/abs/2410.06108
Publikováno v:
Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II. Vol. 13035. SPIE, 2024
In this work, we explore the possibility of using synthetically generated data for video-based gesture recognition with large pre-trained models. We consider whether these models have sufficiently robust and expressive representation spaces to enable
Externí odkaz:
http://arxiv.org/abs/2410.02152
Autor:
Marques, Mateus, Melo, Bruno M. de Souza, Rocha, Alexandre R., Lewenkopf, Caio, da Silva, Luis G. G. V. Dias
We explore the phase diagram of the Mott metal-insulator transition (MIT), focusing on the effects of particle-hole asymmetry (PHA) in the single-band Hubbard model. Our dynamical mean-field theory (DMFT) study reveals that the introduction of PHA in
Externí odkaz:
http://arxiv.org/abs/2409.06674
Active Learning (AL) aims to enhance the performance of deep models by selecting the most informative samples for annotation from a pool of unlabeled data. Despite impressive performance in closed-set settings, most AL methods fail in real-world scen
Externí odkaz:
http://arxiv.org/abs/2312.14126
In this work, we tackle the problem of unsupervised domain adaptation (UDA) for video action recognition. Our approach, which we call UNITE, uses an image teacher model to adapt a video student model to the target domain. UNITE first employs self-sup
Externí odkaz:
http://arxiv.org/abs/2312.02914
Self-supervised Learning (SSL) aims to learn transferable feature representations for downstream applications without relying on labeled data. The Barlow Twins algorithm, renowned for its widespread adoption and straightforward implementation compare
Externí odkaz:
http://arxiv.org/abs/2312.02151
Autor:
Finardi, Paulo, Melo, Wanderley M., Neto, Edgard D. Medeiros, Mansano, Alex F., Costa, Pablo B., Caridá, Vinicius F.
Scarcity of domain-specific data in the Portuguese financial domain has disfavored the development of Natural Language Processing (NLP) applications. To address this limitation, the present study advocates for the utilization of synthetic data genera
Externí odkaz:
http://arxiv.org/abs/2311.11331
Autor:
de Melo, Álvaro M. G., Letellier, Hector, Apoorva, Apoorva, Glicenstein, Antoine, Kaiser, Robin
We report laser frequency stabilization by the combination of modulation transfer spectroscopy and balanced detection of a relatively weak hyperfine transition of the R(158)25-0 line of molecular iodine (${}^{127}$I$_{2}$), which is used as a new fre
Externí odkaz:
http://arxiv.org/abs/2311.08542
We study the problem of human action recognition using motion capture (MoCap) sequences. Unlike existing techniques that take multiple manual steps to derive standardized skeleton representations as model input, we propose a novel Spatial-Temporal Me
Externí odkaz:
http://arxiv.org/abs/2303.18177
Autor:
Reddy, Arun V., Shah, Ketul, Paul, William, Mocharla, Rohita, Hoffman, Judy, Katyal, Kapil D., Manocha, Dinesh, de Melo, Celso M., Chellappa, Rama
Human action recognition is a challenging problem, particularly when there is high variability in factors such as subject appearance, backgrounds and viewpoint. While deep neural networks (DNNs) have been shown to perform well on action recognition t
Externí odkaz:
http://arxiv.org/abs/2303.10280