2D Human Pose Estimation
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/joanrod/paper2figure-dataset
공식 코드https://github.com/johannwyh/styleinv
공식 코드StyleInV: A Temporal Style Modulated Inversion Network for Unconditional Video Generation
https://github.com/johnnlp/phemeplus
공식 코드PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence
https://github.com/jonlin223/raccoon-geocoding
공식 코드https://github.com/josephcao0327/stylefool
공식 코드StyleFool: Fooling Video Classification Systems via Style Transfer
https://github.com/joshzyj/llvms4protest
공식 코드https://github.com/jpelhaW/ParaphraseDetection
공식 코드https://github.com/jpwahle/emnlp22-transforming
공식 코드How Large Language Models are Transforming Machine-Paraphrased Plagiarism
https://github.com/jpwahle/iconf22-paraphrase
공식 코드https://github.com/juanrloaiza/latinamerican-philosophy-mining
공식 코드https://github.com/judydnguyen/pardon-feddg
공식 코드PARDON: Privacy-Aware and Robust Federated Domain Generalization
https://github.com/julian-risch/ICWSM2020
공식 코드Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions
https://github.com/julian-risch/JCDL2018
공식 코드https://github.com/junhocho/paired-glyph-matching
공식 코드https://github.com/junleen/RainNet
공식 코드Region-aware Adaptive Instance Normalization for Image Harmonization
https://github.com/junpan19/ETDM
공식 코드Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling
https://github.com/junpan19/RRN
공식 코드https://github.com/junpan19/RSDN
공식 코드Video Super-Resolution with Recurrent Structure-Detail Network
https://github.com/junpan19/VSR_TGA
공식 코드https://github.com/junpliu/condigsum
공식 코드Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization
https://github.com/junshutang/3DFaceShop
공식 코드3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation
https://github.com/junwang4/linking-health-news-to-pubmed
공식 코드https://github.com/junyanz/CycleGAN
공식 코드Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
공식 코드Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/juseonghan/meshbrush
공식 코드MeshBrush: Painting the Anatomical Mesh with Neural Stylization for Endoscopy
https://github.com/jvladika/improving-health-qa
공식 코드Improving Health Question Answering with Reliable and Time-Aware Evidence Retrieval
https://github.com/jwen307/diamondintherough
공식 코드Diamond in the rough: Improving image realism by traversing the GAN latent space
https://github.com/k-rlange/t2snarrativechanges
공식 코드Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
https://github.com/kaistai/claimdiff
공식 코드ClaimDiff: Comparing and Contrasting Claims on Contentious Issues
https://github.com/kamigaito/wikitig
공식 코드https://github.com/kamruzzaman15/banmani
공식 코드BanMANI: A Dataset to Identify Manipulated Social Media News in Bangla
https://github.com/kapilsprinklr/cxmarena
공식 코드CXMArena: Unified Dataset to benchmark performance in realistic CXM Scenarios
https://github.com/kappakant/SLIM
공식 코드Is Less Really More? Fake News Detection with Limited Information
https://github.com/karlygzhu/rgan
공식 코드A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution
https://github.com/kartikeypant/text-simplification-qa-www2021
공식 코드Text Simplification for Comprehension-based Question-Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.