https://github.com/HLTSingapore/Emotional-Speech-Data
공식 코드Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset
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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.
Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing
Towards Artificial Intelligence Research Assistant for Expert-Involved Learning
Learning State-Aware Visual Representations from Audible Interactions
Name Your Style: An Arbitrary Artist-aware Image Style Transfer
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
AttenST: A Training-Free Attention-Driven Style Transfer Framework with Pre-Trained Diffusion Models
ELLA: Empowering LLMs for Interpretable, Accurate and Informative Legal Advice
FTII-Bench: A Comprehensive Multimodal Benchmark for Flow Text with Image Insertion
WikiCausal: Corpus and Evaluation Framework for Causal Knowledge Graph Construction
Self-Contained Stylization via Steganography for Reverse and Serial Style Transfer
Importance of Copying Mechanism for News Headline Generation
BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization
Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation
EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin Disinformation in News Articles
Quantifying the Relevance of Youth Research Cited in the US Policy Documents
CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer
Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer
FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal Loss
CLOCR-C: Context Leveraging OCR Correction with Pre-trained Language Models
Scrambled text: training Language Models to correct OCR errors using synthetic data
Scrambled text: training Language Models to correct OCR errors using synthetic data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.