https://github.com/MuMiN-dataset/mumin-build
공식 코드MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset
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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.
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset
Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers
Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts
Named Entity Recognition without Labelled Data: A Weak Supervision Approach
PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
Interactive Video Stylization Using Few-Shot Patch-Based Training
SciDTB: Discourse Dependency TreeBank for Scientific Abstracts
Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer
Parameter-Free Style Projection for Arbitrary Style Transfer
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer
Technical Report for Ego4D Long Term Action Anticipation Challenge 2023
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
Priberam at MESINESP Multi-label Classification of Medical Texts Task
Bengali Abstractive News Summarization(BANS): A Neural Attention Approach
Using Titles vs. Full-text as Source for Automated Semantic Document Annotation
UniVST: A Unified Framework for Training-free Localized Video Style Transfer
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
Using Transformer based Ensemble Learning to classify Scientific Articles
Using Transformer based Ensemble Learning to classify Scientific Articles
Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.