Papers with Code 아카이브
Retrieval
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A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Nils-Laurent/RRP-RD
공식 코드https://github.com/NoelShin/reco
공식 코드https://github.com/Nouf-Alabbasi/oKUmura_AI_Telecom_challenge
공식 코드Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP Standards
https://github.com/OFA-Sys/ONE-PEACE
공식 코드ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
https://github.com/OHNLPIR/OMOP_CDM_IO
공식 코드https://github.com/OSM-DM-KGP/Narmada
공식 코드NARMADA: Need and Available Resource Managing Assistant for Disasters and Adversities
https://github.com/OliverZXY/LWSR
공식 코드Lifelong Histopathology Whole Slide Image Retrieval via Distance Consistency Rehearsal
https://github.com/OrangeYHChen/TIPCB
공식 코드TIPCB: A Simple but Effective Part-based Convolutional Baseline for Text-based Person Search
https://github.com/PKU-ICST-MIPL/FGCrossNet_ACMMM2019
공식 코드A New Benchmark and Approach for Fine-grained Cross-media Retrieval
https://github.com/PKUDigitalHealth/KidneyTalk-open
공식 코드https://github.com/PLUM-Lab/AR-RAG
공식 코드AR-RAG: Autoregressive Retrieval Augmentation for Image Generation
https://github.com/PaddlePaddle/ERNIE
공식 코드ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training
https://github.com/PaddlePaddle/Knover/tree/develop/projects/QKConv
공식 코드Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling
https://github.com/PaddlePaddle/PaddleClas
공식 코드https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/multimodal/actbert.md
공식 코드https://github.com/PaddlePaddle/Research
공식 코드HGAMN: Heterogeneous Graph Attention Matching Network for Multilingual POI Retrieval at Baidu Maps
https://github.com/PaddlePaddle/models
공식 코드2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019
https://github.com/PadraigC/kNNTutorial
공식 코드k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)
https://github.com/PapersRepo/partialAutoencoders
공식 코드Phase-Retrieval with Incomplete Autocorrelations Using Deep Convolutional Autoencoders
https://github.com/Paranioar/UniPT
공식 코드UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
https://github.com/ParitoshParmar/Win-Fail-Action-Recognition
공식 코드https://github.com/Parry-Parry/terrier_trec_2023
공식 코드https://github.com/PeihaoChen/RSPNet
공식 코드RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning
https://github.com/PeizhuoLi/walk-the-dog
공식 코드WalkTheDog: Cross-Morphology Motion Alignment via Phase Manifolds
https://github.com/PengBoXiangShang/EdgeMap345C_Dataset
공식 코드On Learning Semantic Representations for Million-Scale Free-Hand Sketches
https://github.com/PhilippeSu/KV-CBO
공식 코드Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning
https://github.com/Pints-AI/Finetune-Bench-RAG
공식 코드Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation
https://github.com/Priberam/mesinesp-svm
공식 코드Priberam at MESINESP Multi-label Classification of Medical Texts Task
https://github.com/Prograf-UFF/HoVW
공식 코드Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval
https://github.com/PruneTruong/DenseMatching
공식 코드PDC-Net+: Enhanced Probabilistic Dense Correspondence Network
https://github.com/PrzemyslawStok/ssakg
공식 코드Associative Knowledge Graphs for Efficient Sequence Storage and Retrieval
https://github.com/Pter61/osrcir
공식 코드https://github.com/PyRGL/rgl
공식 코드RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs
https://github.com/PyRetri/PyRetri
공식 코드https://github.com/QianhaoHan/Cross-Modality-Medical-Report-Generation
공식 코드Integrating MedCLIP and Cross-Modal Fusion for Automatic Radiology Report Generation
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