Search Results for author:
Found 16 papers, 5 papers with code
Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation
Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures.
Temporal and Spacial Studies of Infectious Diseases: Mathematical Models and Numerical Solvers
By introducing space-depend effects such as diffusion and creation in addition to the SIR model, the Fisher's model is in fact a more advanced and comprehensive model.
PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment
In contrast, a pipeline framework first identifies aspects through MATE (Multimodal Aspect Term Extraction) and then aligns these aspects with image patches for sentiment classification (MASC: Multimodal Aspect-Oriented Sentiment Classification).
Aspect-Based Sentiment Analysis
Multimodal Sentiment Analysis
+2
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: Survey on Multimodal Large Language Model
We explore Multimodal Large Language Models (MLLMs), which integrate LLMs like GPT-4 to handle multimodal data, including text, images, audio, and more.
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.
