https://github.com/nadavborenstein/Iggy
공식 코드How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements
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.
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements
Seeing Through AI's Lens: Enhancing Human Skepticism Towards LLM-Generated Fake News
SAVE: Spectral-Shift-Aware Adaptation of Image Diffusion Models for Text-driven Video Editing
GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization
BioSentVec: creating sentence embeddings for biomedical texts
BioSentVec: creating sentence embeddings for biomedical texts
MobileFace: 3D Face Reconstruction with Efficient CNN Regression
HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation
Bilingual BSARD: Extending Statutory Article Retrieval to Dutch
TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge
WikiAsp: A Dataset for Multi-domain Aspect-based Summarization
Anisotropic Stroke Control for Multiple Artists Style Transfer
The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News
Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution
News Article Retrieval in Context for Event-centric Narrative Creation
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select
Learning from Multi-domain Artistic Images for Arbitrary Style Transfer
Towards Exploiting Background Knowledge for Building Conversation Systems
In-Style: Bridging Text and Uncurated Videos with Style Transfer for Text-Video Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.