https://github.com/epfl-dlab/descartes
공식 코드Descartes: Generating Short Descriptions of Wikipedia Articles
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.
Descartes: Generating Short Descriptions of Wikipedia Articles
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
How to Engage Your Readers? Generating Guiding Questions to Promote Active Reading
Human-Guided Fair Classification for Natural Language Processing
SynFinTabs: A Dataset of Synthetic Financial Tables for Information and Table Extraction
Cross-Resolution Flow Propagation for Foveated Video Super-Resolution
An Empirical Study of Mamba-based Pedestrian Attribute Recognition
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations
Intention-Conditioned Long-Term Human Egocentric Action Forecasting
PGB: A PubMed Graph Benchmark for Heterogeneous Network Representation Learning
Classifying Scientific Publications with BERT -- Is Self-Attention a Feature Selection Method?
Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation Detection
Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal Learning
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia
MLQA: Evaluating Cross-lingual Extractive Question Answering
MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition
MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Liputan6: A Large-scale Indonesian Dataset for Text Summarization
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.