161 research outputs found
MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment
The visual quality of point clouds has been greatly emphasized since the
ever-increasing 3D vision applications are expected to provide cost-effective
and high-quality experiences for users. Looking back on the development of
point cloud quality assessment (PCQA) methods, the visual quality is usually
evaluated by utilizing single-modal information, i.e., either extracted from
the 2D projections or 3D point cloud. The 2D projections contain rich texture
and semantic information but are highly dependent on viewpoints, while the 3D
point clouds are more sensitive to geometry distortions and invariant to
viewpoints. Therefore, to leverage the advantages of both point cloud and
projected image modalities, we propose a novel no-reference point cloud quality
assessment (NR-PCQA) metric in a multi-modal fashion. In specific, we split the
point clouds into sub-models to represent local geometry distortions such as
point shift and down-sampling. Then we render the point clouds into 2D image
projections for texture feature extraction. To achieve the goals, the
sub-models and projected images are encoded with point-based and image-based
neural networks. Finally, symmetric cross-modal attention is employed to fuse
multi-modal quality-aware information. Experimental results show that our
approach outperforms all compared state-of-the-art methods and is far ahead of
previous NR-PCQA methods, which highlights the effectiveness of the proposed
method. The code is available at https://github.com/zzc-1998/MM-PCQA
Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images
Computer graphics images (CGIs) are artificially generated by means of
computer programs and are widely perceived under various scenarios, such as
games, streaming media, etc. In practical, the quality of CGIs consistently
suffers from poor rendering during the production and inevitable compression
artifacts during the transmission of multimedia applications. However, few
works have been dedicated to dealing with the challenge of computer graphics
images quality assessment (CGIQA). Most image quality assessment (IQA) metrics
are developed for natural scene images (NSIs) and validated on the databases
consisting of NSIs with synthetic distortions, which are not suitable for
in-the-wild CGIs. To bridge the gap between evaluating the quality of NSIs and
CGIs, we construct a large-scale in-the-wild CGIQA database consisting of 6,000
CGIs (CGIQA-6k) and carry out the subjective experiment in a well-controlled
laboratory environment to obtain the accurate perceptual ratings of the CGIs.
Then, we propose an effective deep learning-based no-reference (NR) IQA model
by utilizing multi-stage feature fusion strategy and multi-stage channel
attention mechanism. The major motivation of the proposed model is to make full
use of inter-channel information from low-level to high-level since CGIs have
apparent patterns as well as rich interactive semantic content. Experimental
results show that the proposed method outperforms all other state-of-the-art NR
IQA methods on the constructed CGIQA-6k database and other CGIQA-related
databases. The database along with the code will be released to facilitate
further research
Learning A Foundation Language Model for Geoscience Knowledge Understanding and Utilization
Large language models (LLMs)have achieved great success in general domains of
natural language processing. In this paper, we bring LLMs to the realm of
geoscience, with the objective of advancing research and applications in this
field. To this end, we present the first-ever LLM in geoscience, K2, alongside
a suite of resources developed to further promote LLM research within
geoscience. For instance, we have curated the first geoscience instruction
tuning dataset, GeoSignal, which aims to align LLM responses to
geoscience-related user queries. Additionally, we have established the first
geoscience benchmark, GeoBenchmark, to evaluate LLMs in the context of
geoscience. In this work, we experiment with a complete recipe to adapt a
pretrained general-domain LLM to the geoscience domain. Specifically, we
further train the LLaMA-7B model on over 1 million pieces of geoscience
literature and utilize GeoSignal's supervised data to fine-tune the model.
Moreover, we share a protocol that can efficiently gather domain-specific data
and construct domain-supervised data, even in situations where manpower is
scarce. Experiments conducted on the GeoBenchmark demonstrate the the
effectiveness of our approach and datasets
The extended growth of graphene oxide flakes using ethanol CVD
We report the extended growth of Graphene Oxide (GO) flakes using atmospheric pressure ethanol Chemical Vapor Deposition (CVD).
GO was used to catalyze the deposition of carbon on substrate in the ethanol CVD with Ar and H2 as carrier gases. Raman, SEM, XPS
10 and AFM characterized the growth to be reduced GO (RGO) of <5 layers. This new grown RGO possesses lower defect density with
larger and increased distribution of sp2 domains than chemically-reduced RGO. Furthermore this method without optimization reduces
relative standard deviation of electrical conductivity between chips, from 80.5% to 16.5%, enabling RGO to be used in practical
electronic devices
Various types of phase transitions in the AdS soliton background
We study the basic holographic insulator and superconductor phase transition
in the AdS soliton background by generalizing the spontaneous breaking of a
global U(1) symmetry to occur via Stckelberg mechanism. We construct
the soliton solutions with backreaction and examine the effects of the
backreaction on the condensation of the scalar hair in the generalized
Stckelberg Lagrangian. We disclose rich physics in various phase
transitions. In addition to the AdS soliton configuration, we also examine the
property of the phase transition in the AdS black hole background.Comment: 11 pages, accepted for publication in Phys.Lett.
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