https://github.com/yangcong356/bita
공식 코드Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image Captioning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image Captioning
Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Prioritizing Image-Related Tokens Enhances Vision-Language Pre-Training
Large Language Model Can Interpret Latent Space of Sequential Recommender
Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations
Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
MEPNet: Medical Entity-balanced Prompting Network for Brain CT Report Generation
Exploring Large Language Models for Knowledge Graph Completion
Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems
Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering
Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs
Housekeep: Tidying Virtual Households using Commonsense Reasoning
Mutually Guided Few-shot Learning for Relational Triple Extraction
Enhancing the Geometric Problem-Solving Ability of Multimodal LLMs via Symbolic-Neural Integration
On the Role of Attention Heads in Large Language Model Safety
FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering
R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models
Spectral Editing of Activations for Large Language Model Alignment
RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding
PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling
A Survey of Graph Meets Large Language Model: Progress and Future Directions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.