https://github.com/lovepiano/SMFN
공식 코드A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
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.
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
A Corpus for Sentence-level Subjectivity Detection on English News Articles
TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugu
StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions
The POLUSA Dataset: 0.9M Political News Articles Balanced by Time and Outlet Popularity
Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation
Sentence Alignment with Parallel Documents Facilitates Biomedical Machine Translation
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer
Fine-grained Text Style Transfer with Diffusion-Based Language Models
P^3SUM: Preserving Author's Perspective in News Summarization with Diffusion Language Models
Article Classification with Graph Neural Networks and Multigraphs
DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles
Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks
LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer
Exploring the structure of a real-time, arbitrary neural artistic stylization network
Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization
Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.