https://github.com/liuxu77/stgcl
공식 코드When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods.
Source: Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?
Asymmetric Visual Semantic Embedding Framework for Efficient Vision-Language Alignment
Reinforcement Learning-powered Semantic Communication via Semantic Similarity
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering
Low-resource Bilingual Dialect Lexicon Induction with Large Language Models
Topology-aware Debiased Self-supervised Graph Learning for Recommendation
Evaluating Document Representations for Content-based Legal Literature Recommendations
Logic Constrained Pointer Networks for Interpretable Textual Similarity
Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks
RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data
Do Vision and Language Encoders Represent the World Similarly?
Efficient Vector Representation for Documents through Corruption
Trend-Aware Fashion Recommendation with Visual Segmentation and Semantic Similarity
Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization
TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual Similarity
Bridging the Gap between Structural and Semantic Similarity in Diverse Planning
SeFNet: Bridging Tabular Datasets with Semantic Feature Nets
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding
Weakly-supervised Audio Separation via Bi-modal Semantic Similarity
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations
Soft Alignment Objectives for Robust Adaptation of Language Generation
Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation
Guiding and Diversifying LLM-Based Story Generation via Answer Set Programming
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.