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Found 8 papers, 2 papers with code
MonoHair: High-Fidelity Hair Modeling from a Monocular Video
For the interior, we employ a data-driven, multi-view 3D hair reconstruction method.
Self-evolving Autoencoder Embedded Q-Network
During the evolution of the autoencoder architecture, a bias-variance regulatory strategy is employed to elicit the optimal response from the RL agent.
UAV 3-D path planning based on MOEA/D with adaptive areal weight adjustment
Unmanned aerial vehicles (UAVs) are desirable platforms for time-efficient and cost-effective task execution.
Multi-ship cooperative air defense model based on queuing theory
Finally, through simulation experiments in typical scenarios, this paper studies and compares the air defense capabilities of the system in two different modes with and without coordination, and verifies the superiority of the multi-ship cooperative air defense model in reducing the probability of missile penetration; Further, the ability changes of the defense system under different parameters such as missile speed, speed, angle, ship interception rate, range, and number of fire units are studied, and the weak points of the defense formation, defense range settings, and interception settings are obtained.
NeuralHDHair: Automatic High-fidelity Hair Modeling from a Single Image Using Implicit Neural Representations
To improve the efficiency of a traditional hair growth algorithm, we adopt a local neural implicit function to grow strands based on the estimated 3D hair geometric features.
Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition
Video-based Unsupervised Domain Adaptation (VUDA) methods improve the robustness of video models, enabling them to be applied to action recognition tasks across different environments.
Multi-Source Video Domain Adaptation with Temporal Attentive Moment Alignment
Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios.
Learn to Steer through Deep Reinforcement Learning
It is worth noting that the developed system is readily transferable from virtual training scenarios to real-world deployment without any fine-tuning by utilizing depth images.
