https://github.com/lzhangbj/DualVar
공식 코드Inter-intra Variant Dual Representations forSelf-supervised Video Recognition
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/lzw108/raemollm
공식 코드https://github.com/lzxlin/knn-bioel
공식 코드Improving Biomedical Entity Linking with Retrieval-enhanced Learning
https://github.com/m-bain/clip-hitchhiker
공식 코드https://github.com/m-bain/frozen-in-time
공식 코드Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
https://github.com/m2man/HADA-LAVIS
공식 코드HADA: A Graph-based Amalgamation Framework in Image-text Retrieval
https://github.com/m2man/LGSGM
공식 코드A Deep Local and Global Scene-Graph Matching for Image-Text Retrieval
https://github.com/m3hrdadfi/rag-memory-interplay
공식 코드https://github.com/ma787639046/bowdpr
공식 코드Drop your Decoder: Pre-training with Bag-of-Word Prediction for Dense Passage Retrieval
https://github.com/ma787639046/tdro
공식 코드Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval
https://github.com/maastrichtlawtech/bsard
공식 코드https://github.com/maastrichtlawtech/fusion
공식 코드Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain
https://github.com/maastrichtlawtech/gdsr
공식 코드Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks
https://github.com/maastrichtlawtech/lleqa
공식 코드Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models
https://github.com/mabergerx/SDP500_expert_search
공식 코드Effective Distributed Representations for Academic Expert Search
https://github.com/machuangtao/kg-rag4sm
공식 코드Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching
https://github.com/maciejzieba/binGAN
공식 코드BinGAN: Learning Compact Binary Descriptors with a Regularized GAN
https://github.com/magic-ai4med/kep
공식 코드Knowledge-enhanced Visual-Language Pretraining for Computational Pathology
https://github.com/magiccircuit/enhance-finegrained
공식 코드https://github.com/magics-lab/outeffhop
공식 코드Outlier-Efficient Hopfield Layers for Large Transformer-Based Models
https://github.com/magics-lab/sparsemodernhopfield
공식 코드https://github.com/magics-lab/uhop
공식 코드Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models
https://github.com/magkai/CROWN
공식 코드Conversational Question Answering over Passages by Leveraging Word Proximity Networks
https://github.com/magkai/PRAISE
공식 코드Preference-based Learning with Retrieval Augmented Generation for Conversational Question Answering
https://github.com/mahmoodlab/FISH
공식 코드https://github.com/mahmoodlab/titan
공식 코드https://github.com/mahmoodlab/uni
공식 코드https://github.com/mainaksingha01/splip
공식 코드Elevating All Zero-Shot Sketch-Based Image Retrieval Through Multimodal Prompt Learning
https://github.com/mainlp/codeswitchclir
공식 코드Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data
https://github.com/mainlp/nnose
공식 코드https://github.com/mainlp/wikidir
공식 코드Cross-Dialect Information Retrieval: Information Access in Low-Resource and High-Variance Languages
https://github.com/mainsample1234/as-gir
공식 코드ASGIR: Audio Spectrogram Transformer Guided Classification And Information Retrieval For Birds
https://github.com/makexine/CDIT
공식 코드Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs
https://github.com/malidib/Craters_MaskRCNN
공식 코드Automated crater shape retrieval using weakly-supervised deep learning
https://github.com/malllabiisc/NeuralDater
공식 코드https://github.com/malongxuan/msd
공식 코드I run as fast as a rabbit, can you? A Multilingual Simile Dialogue Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.