Zobrazeno 1 - 10
of 597
pro vyhledávání: '"Liu, Meiqin"'
Video compression aims to reconstruct seamless frames by encoding the motion and residual information from existing frames. Previous neural video compression methods necessitate distinct codecs for three types of frames (I-frame, P-frame and B-frame)
Externí odkaz:
http://arxiv.org/abs/2405.14336
DETR-like methods have significantly increased detection performance in an end-to-end manner. The mainstream two-stage frameworks of them perform dense self-attention and select a fraction of queries for sparse cross-attention, which is proven effect
Externí odkaz:
http://arxiv.org/abs/2403.16131
As a critical clue of video super-resolution (VSR), inter-frame alignment significantly impacts overall performance. However, accurate pixel-level alignment is a challenging task due to the intricate motion interweaving in the video. In response to t
Externí odkaz:
http://arxiv.org/abs/2312.07823
This article introduces a five-tiered route planner for accessing multiple nodes with multiple autonomous underwater vehicles (AUVs) that enables efficient task completion in stochastic ocean environments. First, the pre-planning tier solves the sing
Externí odkaz:
http://arxiv.org/abs/2311.06579
Learned B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. However, previous learned approaches often directly extend neural P-frame codecs to B-frame relyin
Externí odkaz:
http://arxiv.org/abs/2309.13835
Cooperative online scalar field mapping is an important task for multi-robot systems. Gaussian process regression is widely used to construct a map that represents spatial information with confidence intervals. However, it is difficult to handle coop
Externí odkaz:
http://arxiv.org/abs/2309.10311
Publikováno v:
IEEE Robotics and Automation Letters, 2023
In this paper, we consider improving the efficiency of information-based autonomous robot exploration in unknown and complex environments. We first utilize Gaussian process (GP) regression to learn a surrogate model to infer the confidence-rich mutua
Externí odkaz:
http://arxiv.org/abs/2309.05200
This paper investigates the problem of distributed target tracking via underwater wireless sensor networks (UWSNs) with fading channels. The degradation of signal quality due to wireless channel fading can significantly impact network reliability and
Externí odkaz:
http://arxiv.org/abs/2308.04013
In this paper, physics-informed neural network (PINN) based on characteristic-based split (CBS) is proposed, which can be used to solve the time-dependent Navier-Stokes equations (N-S equations). In this method, The output parameters and correspondin
Externí odkaz:
http://arxiv.org/abs/2304.10717
Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multi-class image-to-image (3D-aware I2I) t
Externí odkaz:
http://arxiv.org/abs/2303.15012