https://github.com/giovana-morais/steme
공식 코드Tempo vs. Pitch: understanding self-supervised tempo estimation
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 11
- 보존된 평가 기록
- 19
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/giovannitra/multimodalneuraldatabases
공식 코드https://github.com/gipplab/cl-osa
공식 코드Detecting Cross-Language Plagiarism using Open Knowledge Graphs
https://github.com/gissygonzalez/kleiogpt
공식 코드If the Sources Could Talk: Evaluating Large Language Models for Research Assistance in History
https://github.com/git-disl/seje
공식 코드https://github.com/git4review/conformal_ranked_retrieval
공식 코드https://github.com/gitbosun/patchrd
공식 코드PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation
https://github.com/github/CodeSearchNet
공식 코드CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
https://github.com/gk0wk/xgraphrag
공식 코드XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation
https://github.com/gkordo/s2vs
공식 코드https://github.com/gkouros/contrastive-pose-retrieval
공식 코드Category-Level Pose Retrieval with Contrastive Features Learnt with Occlusion Augmentation
https://github.com/gladcolor/llm-find
공식 코드An Autonomous GIS Agent Framework for Geospatial Data Retrieval
https://github.com/gladcolor/llm-geo
공식 코드https://github.com/glee4810/EHRSQL
공식 코드EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
https://github.com/glennq/instrument-recognition
공식 코드Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks
https://github.com/glovo/foodi-ml-dataset
공식 코드FooDI-ML: a large multi-language dataset of food, drinks and groceries images and descriptions
https://github.com/gmago-leway/fewshotgec
공식 코드Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction
https://github.com/gmberton/auto_vpr
공식 코드EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition
https://github.com/gmberton/earthloc
공식 코드EarthLoc: Astronaut Photography Localization by Indexing Earth from Space
https://github.com/gmberton/eigenplaces
공식 코드EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition
https://github.com/gmberton/geo_warp
공식 코드Viewpoint Invariant Dense Matching for Visual Geolocalization
https://github.com/gmberton/image-retrieval
공식 코드https://github.com/gmberton/megaloc
공식 코드https://github.com/gmftbyGMFTBY/HashRetrieval
공식 코드https://github.com/gmftbygmftby/copyisallyouneed
공식 코드https://github.com/gmftbygmftby/simpleredial-v1
공식 코드https://github.com/gofigure-lanl/figure-segmentation
공식 코드DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding
https://github.com/gofynd/mildnet
공식 코드MILDNet: A Lightweight Single Scaled Deep Ranking Architecture
https://github.com/gomate-community/trustrag
공식 코드TrustRAG: An Information Assistant with Retrieval Augmented Generation
https://github.com/gombru/LearnFromWebData
공식 코드Learning to Learn from Web Data through Deep Semantic Embeddings
https://github.com/gonced8/document-summarization
공식 코드https://github.com/gonced8/rachael-scai
공식 코드Question rewriting? Assessing its importance for conversational question answering
https://github.com/gongzix/lite-mind
공식 코드Lite-Mind: Towards Efficient and Robust Brain Representation Network
https://github.com/goodproj13/DenserNet
공식 코드DenserNet: Weakly Supervised Visual Localization Using Multi-scale Feature Aggregation
https://github.com/google-deepmind/loft
공식 코드Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
https://github.com/google-deepmind/magiclens
공식 코드MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.