https://github.com/xiaoachen98/DDB
공식 코드Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
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.
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation
ConRF: Zero-shot Stylization of 3D Scenes with Conditioned Radiation Fields
An Animation-based Augmentation Approach for Action Recognition from Discontinuous Video
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization
Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia
Dynamic Global Memory for Document-level Argument Extraction
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
Towards Using Clothes Style Transfer for Scenario-aware Person Video Generation
StainGAN: Stain Style Transfer for Digital Histological Images
TMP: Temporal Motion Propagation for Online Video Super-Resolution
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer
LKPNR: LLM and KG for Personalized News Recommendation Framework
Doubly Abductive Counterfactual Inference for Text-based Image Editing
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation
Local-Global Temporal Difference Learning for Satellite Video Super-Resolution
Local-Global Temporal Difference Learning for Satellite Video Super-Resolution
MDCure: A Scalable Pipeline for Multi-Document Instruction-Following
SciDQA: A Deep Reading Comprehension Dataset over Scientific Papers
News Recommendation with Category Description by a Large Language Model
Enhancing Keyphrase Extraction from Academic Articles Using Section Structure Information
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization
None Class Ranking Loss for Document-Level Relation Extraction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.