Zobrazeno 1 - 10
of 66 431
pro vyhledávání: '"Gül A."'
Fundamental Measure Theory (FMT) is a successful and versatile approach for describing the properties of the hard-sphere fluid and hard-sphere mixtures within the framework of classical density functional theory (DFT). Lutsko [Phys. Rev. E 102, 06213
Externí odkaz:
http://arxiv.org/abs/2409.01750
Autor:
Linden, Alex, Gül, Betül
Postselection is an operation that allows the selection of specific measurement outcomes. It serves as a powerful theoretical tool for enhancing the performance of existing quantum algorithms. Despite recent developments such as time reversal in quan
Externí odkaz:
http://arxiv.org/abs/2409.03785
The ever-evolving landscape of wireless communication technologies has led to the development of 5G-NR (5G New Radio) networks promising higher data rates and lower latency. However, with these advancements come challenges in managing intra-cell and
Externí odkaz:
http://arxiv.org/abs/2408.14097
In this paper, we present a novel approach to interference detection in 5G New Radio (5G-NR) networks using Convolutional Neural Networks (CNN). Interference in 5G networks challenges high-quality service due to dense user equipment deployment and in
Externí odkaz:
http://arxiv.org/abs/2408.11659
The focus of this paper is on 3D motion editing. Given a 3D human motion and a textual description of the desired modification, our goal is to generate an edited motion as described by the text. The key challenges include the scarcity of training dat
Externí odkaz:
http://arxiv.org/abs/2408.00712
Autor:
Xie, Junyu, Han, Tengda, Bain, Max, Nagrani, Arsha, Varol, Gül, Xie, Weidi, Zisserman, Andrew
Our objective is to generate Audio Descriptions (ADs) for both movies and TV series in a training-free manner. We use the power of off-the-shelf Visual-Language Models (VLMs) and Large Language Models (LLMs), and develop visual and text prompting str
Externí odkaz:
http://arxiv.org/abs/2407.15850
Autor:
De Smet, Maxim, Matsumoto, Yuta, Zwerver, Anne-Marije J., Tryputen, Larysa, de Snoo, Sander L., Amitonov, Sergey V., Sammak, Amir, Samkharadze, Nodar, Gül, Önder, Wasserman, Rick N. M., Rimbach-Russ, Maximilian, Scappucci, Giordano, Vandersypen, Lieven M. K.
The computational power and fault-tolerance of future large-scale quantum processors derive in large part from the connectivity between the qubits. One approach to increase connectivity is to engineer qubit-qubit interactions at a distance. Alternati
Externí odkaz:
http://arxiv.org/abs/2406.07267
We provide results of our study on text-based 3D human motion retrieval and particularly focus on cross-dataset generalization. Due to practical reasons such as dataset-specific human body representations, existing works typically benchmarkby trainin
Externí odkaz:
http://arxiv.org/abs/2405.16909
Autor:
Raude, Charles, Prajwal, K R, Momeni, Liliane, Bull, Hannah, Albanie, Samuel, Zisserman, Andrew, Varol, Gül
In this work, our goals are two fold: large-vocabulary continuous sign language recognition (CSLR), and sign language retrieval. To this end, we introduce a multi-task Transformer model, CSLR2, that is able to ingest a signing sequence and output in
Externí odkaz:
http://arxiv.org/abs/2405.10266
We describe a protocol to study text-to-video retrieval training with unlabeled videos, where we assume (i) no access to labels for any videos, i.e., no access to the set of ground-truth captions, but (ii) access to labeled images in the form of text
Externí odkaz:
http://arxiv.org/abs/2404.17498