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Found 7 papers, 0 papers with code
MuseGraph: Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining
Traditional Graph Neural Networks (GNNs), which are commonly used for modeling attributed graphs, need to be re-trained every time when applied to different graph tasks and datasets.
SARA: Controllable Makeup Transfer with Spatial Alignment and Region-Adaptive Normalization
To address this problem, we propose a novel Spatial Alignment and Region-Adaptive normalization method (SARA) in this paper.
Geo-Context Aware Study of Vision-Based Autonomous Driving Models and Spatial Video Data
The visual study is seamlessly integrated with the geographical environment by combining DL model performance with geospatial visualization techniques.
Towards Annotation-Free Evaluation of Cross-Lingual Image Captioning
For the first scenario with the references available, we propose two metrics, i. e., WMDRel and CLinRel.
Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images
Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images.
Publicly Verifiable Databases With All Efficient Updating Operations
As a result, it remains an open problem how to construct an efficient (and publicly verifiable) VDB scheme that can support all updating operations regardless of the manner of insertion.
Visual Understanding of Multiple Attributes Learning Model of X-Ray Scattering Images
This extended abstract presents a visualization system, which is designed for domain scientists to visually understand their deep learning model of extracting multiple attributes in x-ray scattering images.
