Zobrazeno 1 - 10
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pro vyhledávání: '"Qin Zheng"'
Quantum entanglement detection and characterization are crucial for various quantum information processes. Most existing methods for entanglement detection rely heavily on a complete description of the quantum state, which requires numerous measureme
Externí odkaz:
http://arxiv.org/abs/2408.13015
Machine-Learning Insights on Entanglement-trainability Correlation of Parameterized Quantum Circuits
Variational quantum algorithms (VQAs) have emerged as the leading strategy to obtain quantum advantage on the current noisy intermediate-scale devices. However, their entanglement-trainability correlation, as the major reason for the barren plateau (
Externí odkaz:
http://arxiv.org/abs/2406.01997
Multi-Object Tracking MOT encompasses various tracking scenarios, each characterized by unique traits. Effective trackers should demonstrate a high degree of generalizability across diverse scenarios. However, existing trackers struggle to accommodat
Externí odkaz:
http://arxiv.org/abs/2406.00429
This paper focuses on training a robust RGB-D registration model without ground-truth pose supervision. Existing methods usually adopt a pairwise training strategy based on differentiable rendering, which enforces the photometric and the geometric co
Externí odkaz:
http://arxiv.org/abs/2405.00507
AI-generated content has accelerated the topic of media synthesis, particularly Deepfake, which can manipulate our portraits for positive or malicious purposes. Before releasing these threatening face images, one promising forensics solution is the i
Externí odkaz:
http://arxiv.org/abs/2404.17867
Noisy label learning aims to learn robust networks under the supervision of noisy labels, which plays a critical role in deep learning. Existing work either conducts sample selection or label correction to deal with noisy labels during the model trai
Externí odkaz:
http://arxiv.org/abs/2404.10499
Multi-instance point cloud registration estimates the poses of multiple instances of a model point cloud in a scene point cloud. Extracting accurate point correspondence is to the center of the problem. Existing approaches usually treat the scene poi
Externí odkaz:
http://arxiv.org/abs/2404.04557
Autor:
Zhang, Xiaocai, Fu, Xiuju, Xiao, Zhe, Xu, Haiyan, Wei, Xiaoyang, Koh, Jimmy, Ogawa, Daichi, Qin, Zheng
This paper investigates the prediction of vessels' arrival time to the pilotage area using multi-data fusion and deep learning approaches. Firstly, the vessel arrival contour is extracted based on Multivariate Kernel Density Estimation (MKDE) and clu
Externí odkaz:
http://arxiv.org/abs/2403.09969
Deepfake videos are becoming increasingly realistic, showing few tampering traces on facial areasthat vary between frames. Consequently, existing Deepfake detection methods struggle to detect unknown domain Deepfake videos while accurately locating t
Externí odkaz:
http://arxiv.org/abs/2401.13516
Quantum entanglement plays a pivotal role in various quantum information processing tasks. However, there still lacks a universal and effective way to detecting entanglement structures, especially for high-dimensional and multipartite quantum systems
Externí odkaz:
http://arxiv.org/abs/2401.03400