https://github.com/fastnlp/style-transformer
공식 코드Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
What is Human Pose Estimation? Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Background. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. The reason for its importance is the abundance of applications that can benefit from such a technology. For example, human pose estimation allows for higher-level reasoning in the context of human-computer interaction and activity recognition; it is also one of the basic building blocks for marker-less motion capture (MoCap) technology. MoCap technology is useful for applications ranging from character animation to clinical analysis of gait pathologies.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation
VAE based Text Style Transfer with Pivot Words Enhancement Learning
Learning Landmarks Motion from Speech for Speaker-Agnostic 3D Talking Heads Generation
Bridging Unpaired Facial Photos And Sketches By Line-drawings
A diverse Multilingual News Headlines Dataset from around the World
RLMiniStyler: Light-weight RL Style Agent for Arbitrary Sequential Neural Style Generation
Giveme5W1H: A Universal System for Extracting Main Events from News Articles
Towards Target-dependent Sentiment Classification in News Articles
Improving Entity Linking through Semantic Reinforced Entity Embeddings
Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions
Intent Factored Generation: Unleashing the Diversity in Your Language Model
Non-Parametric Memory Guidance for Multi-Document Summarization
A general method for estimating the prevalence of Influenza-Like-Symptoms with Wikipedia data
Enriched BERT Embeddings for Scholarly Publication Classification
Action Scene Graphs for Long-Form Understanding of Egocentric Videos
Predicting Sentence-Level Factuality of News and Bias of Media Outlets
FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models
Leveraging Dependency Forest for Neural Medical Relation Extraction
A MISMATCHED Benchmark for Scientific Natural Language Inference
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Good News, Everyone! Context driven entity-aware captioning for news images
Language agents achieve superhuman synthesis of scientific knowledge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.