Zobrazeno 1 - 10
of 9 956
pro vyhledávání: '"Liu,Di"'
Autor:
Bushmakin, Vladislav, von Berg, Oliver, Sauerzapf, Colin, Jayaram, Sreehari, Denisenko, Andrej, Vorobyov, Vadim, Gerhardt, Ilja, Liu, Di, Wrachtrup, Jörg
Scalable quantum networks rely on optical connections between long-lived qubits to distribute entanglement. Tin vacancies in diamond have emerged as promising long-lived qubits, offering extended spin coherence times at liquid helium temperatures and
Externí odkaz:
http://arxiv.org/abs/2412.17539
Temporal action localization (TAL) involves dual tasks to classify and localize actions within untrimmed videos. However, the two tasks often have conflicting requirements for features. Existing methods typically employ separate heads for classificat
Externí odkaz:
http://arxiv.org/abs/2412.09202
Autor:
Feng, Lan-Tian, Zhang, Ming, Liu, Di, Cheng, Yu-Jie, Song, Xin-Yu, Ding, Yu-Yang, Dai, Dao-Xin, Guo, Guo-Ping, Guo, Guang-Can, Ren, Xi-Feng
Quantum networks provide a novel framework for quantum information processing, significantly enhancing system capacity through the interconnection of modular quantum nodes. Beyond the capability to distribute quantum states, the ability to remotely c
Externí odkaz:
http://arxiv.org/abs/2411.15444
Deep neural networks (DNNs) have recently achieved impressive success across a wide range of real-world vision and language processing tasks, spanning from image classification to many other downstream vision tasks, such as object detection, tracking
Externí odkaz:
http://arxiv.org/abs/2411.01431
Autor:
Steidl, Timo, Kuna, Pierre, Hesselmeier-Hüttmann, Erik, Liu, Di, Stöhr, Rainer, Knolle, Wolfgang, Ghezellou, Misagh, Ul-Hassan, Jawad, Schober, Maximilian, Bockstedte, Michel, Gali, Adam, Vorobyov, Vadim, Wrachtrup, Jörg
Nanoelectrical and photonic integration of quantum optical components is crucial for scalable solid-state quantum technologies. Silicon carbide stands out as a material with mature quantum defects and a wide variety of applications in semiconductor i
Externí odkaz:
http://arxiv.org/abs/2410.09021
Autor:
He, Xiaoxiao, Han, Ligong, Dao, Quan, Wen, Song, Bai, Minhao, Liu, Di, Zhang, Han, Min, Martin Renqiang, Juefei-Xu, Felix, Tan, Chaowei, Liu, Bo, Li, Kang, Li, Hongdong, Huang, Junzhou, Ahmed, Faez, Srivastava, Akash, Metaxas, Dimitris
Discrete diffusion models have achieved success in tasks like image generation and masked language modeling but face limitations in controlled content editing. We introduce DICE (Discrete Inversion for Controllable Editing), the first approach to ena
Externí odkaz:
http://arxiv.org/abs/2410.08207
Autor:
Diao, Wenhui, Yu, Haichen, Kang, Kaiyue, Ling, Tong, Liu, Di, Feng, Yingchao, Bi, Hanbo, Ren, Libo, Li, Xuexue, Mao, Yongqiang, Sun, Xian
Aerial Remote Sensing (ARS) vision tasks pose significant challenges due to the unique characteristics of their viewing angles. Existing research has primarily focused on algorithms for specific tasks, which have limited applicability in a broad rang
Externí odkaz:
http://arxiv.org/abs/2409.13366
Autor:
Liu, Di, Chen, Meng, Lu, Baotong, Jiang, Huiqiang, Han, Zhenhua, Zhang, Qianxi, Chen, Qi, Zhang, Chengruidong, Ding, Bailu, Zhang, Kai, Chen, Chen, Yang, Fan, Yang, Yuqing, Qiu, Lili
Transformer-based Large Language Models (LLMs) have become increasingly important. However, due to the quadratic time complexity of attention computation, scaling LLMs to longer contexts incurs extremely slow inference latency and high GPU memory con
Externí odkaz:
http://arxiv.org/abs/2409.10516
Leveraging multiple training datasets to scale up image segmentation models is beneficial for increasing robustness and semantic understanding. Individual datasets have well-defined ground truth with non-overlapping mask layouts and mutually exclusiv
Externí odkaz:
http://arxiv.org/abs/2409.09893
Autor:
Gander, Martin Jakob, Lu, Liu-Di
We present here the classical Schwarz method with a time domain decomposition applied to unconstrained parabolic optimal control problems. Unlike Dirichlet-Neumann and Neumann-Neumann algorithms, we find different properties based on the forward-back
Externí odkaz:
http://arxiv.org/abs/2408.12512