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Found 20 papers, 10 papers with code
The KnowWhereGraph Ontology
KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs.
Turning Generative Models Degenerate: The Power of Data Poisoning Attacks
Moreover, we demonstrate that existing popular defenses are ineffective against our poisoning attacks.
Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks
Growing applications of large language models (LLMs) trained by a third party raise serious concerns on the security vulnerability of LLMs. It has been demonstrated that malicious actors can covertly exploit these vulnerabilities in LLMs through poisoning attacks aimed at generating undesirable outputs.
Here Is Not There: Measuring Entailment-Based Trajectory Similarity for Location-Privacy Protection and Beyond
In this work, we present a different view on trajectory similarity by introducing a measure that utilizes logical entailment.
Towards General-Purpose Representation Learning of Polygonal Geometries
For the spatial domain approach, we propose ResNet1D, a 1D CNN-based polygon encoder, which uses circular padding to achieve loop origin invariance on simple polygons.
Narrative Cartography with Knowledge Graphs
With the help of this tool, the retrieved data from KGs are directly materialized in a GIS format which is ready for spatial analysis and mapping.
Time in a Box: Advancing Knowledge Graph Completion with Temporal Scopes
Hence, knowledge base completion (KBC) on temporal knowledge bases (TKB), where each statement \textit{may} be associated with a temporal scope, has attracted growing attention.
A Review of Location Encoding for GeoAI: Methods and Applications
A common need for artificial intelligence models in the broader geoscience is to represent and encode various types of spatial data, such as points (e. g., points of interest), polylines (e. g., trajectories), polygons (e. g., administrative regions), graphs (e. g., transportation networks), or rasters (e. g., remote sensing images), in a hidden embedding space so that they can be readily incorporated into deep learning models.
Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions
As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language.
Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019
Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything.
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
In this work we propose a novel deep learning architecture called Traffic Transformer to capture the continuity and periodicity of time series and to model spatial dependency.
Ranked #1 on
Time Series Forecasting
on Consumer Spendings
SEKD: Self-Evolving Keypoint Detection and Description
Researchers have attempted utilizing deep neural network (DNN) to learn novel local features from images inspired by its recent successes on a variety of vision tasks.
Accelerating Neural Network Inference by Overflow Aware Quantization
The inherent heavy computation of deep neural networks prevents their widespread applications.
SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting
We also construct a geographic knowledge graph as well as a set of geographic query-answer pairs called DBGeo to evaluate the performance of SE-KGE in comparison to multiple baselines.
Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online
In the geospatial aspect, we propose to enrich a query by using both place partonomy and distance decay.
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells
The key idea is to use neural networks to convert words in texts to vector space representations based on word positions in a sentence and their contexts, which are suitable for end-to-end training of downstream tasks.
TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction
Link prediction is an important and frequently studied task that contributes to an understanding of the structure of knowledge graphs (KGs) in statistical relational learning.
Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs
Recently, several studies have explored methods for using KG embedding to answer logical queries.
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
Finally, we gather up the decoder layers with equivalent scales (sizes) to develop a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels.
Ranked #146 on
Object Detection
on COCO test-dev
CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving
The ability to detect small objects and the speed of the object detector are very important for the application of autonomous driving, and in this paper, we propose an effective yet efficient one-stage detector, which gained the second place in the Road Object Detection competition of CVPR2018 workshop - Workshop of Autonomous Driving(WAD).
