https://github.com/znhy1024/justilm
공식 코드JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims
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.
JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims
Large-scale Evaluation of Transformer-based Article Encoders on the Task of Citation Recommendation
Paragraph-level Citation Recommendation based on Topic Sentences as Queries
Less is More: Rejecting Unreliable Reviews for Product Question Answering
Can Large Language Models Match the Conclusions of Systematic Reviews?
Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning
FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer
Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study
Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons & Dragons
Pay Attention to Your Tone: Introducing a New Dataset for Polite Language Rewrite
Identifying the style by a qualified reader on a short fragment of generated poetry
How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions
Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
Readability-based Sentence Ranking for Evaluating Text Simplification
News Session-Based Recommendations using Deep Neural Networks
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.