https://github.com/zhaoyue-zephyrus/testra
공식 코드Real-time Online Video Detection with Temporal Smoothing Transformers
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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.
Real-time Online Video Detection with Temporal Smoothing Transformers
Reasoning about Goals, Steps, and Temporal Ordering with WikiHow
DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
LOVECon: Text-driven Training-Free Long Video Editing with ControlNet
ETNet: Error Transition Network for Arbitrary Style Transfer
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack
Show, Write, and Retrieve: Entity-aware Article Generation and Retrieval
WIKIGENBENCH: Exploring Full-length Wikipedia Generation under Real-World Scenario
Deformable One-shot Face Stylization via DINO Semantic Guidance
Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives
SpectralCLIP: Preventing Artifacts in Text-Guided Style Transfer from a Spectral Perspective
LogoStyleFool: Vitiating Video Recognition Systems via Logo Style Transfer
Structured Sparsity Learning for Efficient Video Super-Resolution
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion
ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval
Defending against Model Stealing via Verifying Embedded External Features
Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset
PEaCE: A Chemistry-Oriented Dataset for Optical Character Recognition on Scientific Documents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.