Search Results for author:
Found 13 papers, 4 papers with code
DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking
Multimodal entity linking (MEL) aims to utilize multimodal information (usually textual and visual information) to link ambiguous mentions to unambiguous entities in knowledge base.
Causal Graph Neural Networks for Wildfire Danger Prediction
In that direction, we propose integrating causality with Graph Neural Networks (GNNs) that explicitly model the causal mechanism among complex variables via graph learning.
Efficient Subseasonal Weather Forecast using Teleconnection-informed Transformers
Subseasonal forecasting, which is pivotal for agriculture, water resource management, and early warning of disasters, faces challenges due to the chaotic nature of the atmosphere.
A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking
Multimodal Entity Linking (MEL) aims at linking ambiguous mentions with multimodal information to entity in Knowledge Graph (KG) such as Wikipedia, which plays a key role in many applications.
How to Bridge the Gap between Modalities: A Comprehensive Survey on Multimodal Large Language Model
The study surveys existing modal alignment methods in MLLMs into four groups: (1) Multimodal Converters that change data into something LLMs can understand; (2) Multimodal Perceivers to improve how LLMs perceive different types of data; (3) Tools Assistance for changing data into one common format, usually text; and (4) Data-Driven methods that teach LLMs to understand specific types of data in a dataset.
Exploring Geometric Deep Learning For Precipitation Nowcasting
Motivated by this, we explore a geometric deep learning-based temporal Graph Convolutional Network (GCN) for precipitation nowcasting.
Rt-Track: Robust Tricks for Multi-Pedestrian Tracking
However, modeling the motion and appearance models of objects in complex scenes still faces various challenging issues.
InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition
In the field of face recognition, it is always a hot research topic to improve the loss solution to make the face features extracted by the network have greater discriminative power.
Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale Transformer
We propose a semi-supervised network for wide-angle portraits correction.
Reiterative Domain Aware Multi-Target Adaptation
Multi-target domain adaptation is a powerful extension in which a single classifier is learned for multiple unlabeled target domains.
Practical Wide-Angle Portraits Correction with Deep Structured Models
Wide-angle portraits often enjoy expanded views.
Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction
The network is carefully constructed by stacking multiple attention units in depth to fully model dense interactions over token-label spaces, in which two basic attention units are proposed to explicitly capture fine-grained correlations across different modalities (e. g., token-to-token and labelto-token).
The Utility of General Domain Transfer Learning for Medical Language Tasks
The BERT models using either set of pretrained checkpoints outperformed the logistic regression model, achieving sample-weighted average F1-scores of 0. 87 and 0. 87 for the general domain model and the combined general and biomedical-domain model.
