Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/QinggangSUN/keras_complex_valued_networks
https://github.com/QustKcz/relational-GCN
https://github.com/RPraneetha/MoCo-FAIR
Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
https://github.com/RUCAIBox/R1-Searcher
ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process Rewarding
https://github.com/Raman-Raje/Machine-Reading-Comprehension-Neural-Question-Answer-
https://github.com/Raykoooo/3rd_place_to_Kaggle_Google_Landmark_Retrieval_2020
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
https://github.com/Recognito-Vision/Face-SDK-Linux-Demos
https://github.com/ReemHal/Browser-Based-Annotator
https://github.com/Rishit-dagli/Compositional-Attention
https://github.com/Robinlovelace/integrating-gat
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
https://github.com/Robinlovelace/open-gat
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
https://github.com/RocketFlash/easy_metric_learning/tree/master/tools
Unicom: Universal and Compact Representation Learning for Image Retrieval
https://github.com/RomFas/PRGE
iASiS Open Data Graph: Automated Semantic Integration of Disease-Specific Knowledge
https://github.com/RuibinMa/comp755project-ruibinma
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
https://github.com/RyanLiGod/multiple_target_hnswlib
Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors
https://github.com/SCP-173-cool/match_rcnn
https://github.com/SDLZY/VCR_Align
https://github.com/SKorablyov/MaxProblem
https://github.com/SKorablyov/Pranam
https://github.com/Samjoel3101/Self-Supervised-Learning-fastai2
Combination of Multiple Global Descriptors for Image Retrieval
https://github.com/Saswati08/Triplet-Networks-with-GANs
https://github.com/ScaDS/Match-R-CNN-Repoduction
https://github.com/SforAiDl/CountCLIP
https://github.com/ShashSec/SMTI_SA
Towards a relation extraction framework for cyber-security concepts
https://github.com/Shiro-LK/python-DOLG
Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
https://github.com/ShivaKrishnaM/ZS-SBIR
https://github.com/ShyamSubramanian/HESM
Combining Fact Extraction and Verification with Neural Semantic Matching Networks
https://github.com/SilverIonn/EPHN-OpenSet
https://github.com/Sorananakiii/Fine-grained-image-recognition
https://github.com/SuYindu/TACLR
https://github.com/Suoivy/LPD-net
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis
https://github.com/SuperpoweredAI/spRAG
FinanceBench: A New Benchmark for Financial Question Answering
https://github.com/SurajDonthi/Clean-ST-ReID-Multi-Target-Multi-Camera-Tracking
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/SurajDonthi/Multi-Camera-Person-Re-Identification
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.