Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/mikacuy/deformation_aware_embedding
https://github.com/mikacuy/joint_learning_retrieval_deformation
https://github.com/mikejqzhang/SituatedQA
https://github.com/mikvrax/TrecingLab
https://github.com/mindspore-ai/models/tree/master/research/cv/DDAG
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification
https://github.com/mindspore-ai/models/tree/master/research/cv/delf
Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/mindspore-ai/models/tree/master/research/cv/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/mindspore-courses/d2l-mindspore/tree/master/chapter_15_natural_language_processing_applications
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/mindspore-courses/heads-on-mindspore/tree/main/4-neural_poet_RNN
A Critical Review of Recurrent Neural Networks for Sequence Learning
https://github.com/mitjanikolaus/compositional-image-captioning
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/mjhucla/TF-mRNN
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
https://github.com/ml-jku/mhn-react
https://github.com/mlfoundations/open_clip
https://github.com/mniepert/mmkb
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
https://github.com/moabitcoin/sisyphus
Particular object retrieval with integral max-pooling of CNN activations
https://github.com/modelscope/mcpbench
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
https://github.com/modelscope/modelscope
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
https://github.com/mohammadpz/Associative_LSTM
https://github.com/mop/bier
Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly
https://github.com/morehovschi/drumsep
https://github.com/morrisxu-driving/video-music_cross-modal_retrival
Content-Based Video-Music Retrieval Using Soft Intra-Modal Structure Constraint
https://github.com/mostafaramadann/Single-Image-3D-reconstruction
https://github.com/mrezende/cbsoft-sbes-2020-short-paper
CoNCRA: A Convolutional Neural Network Code Retrieval Approach
https://github.com/msfuxian/SEMICON
SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval
https://github.com/msgwak/last
https://github.com/msight-tech/research-xbm
https://github.com/muennighoff/sgpt
https://github.com/mustafa1728/TA3N-Lightning
https://github.com/mustafaaljadery/gemma-2b-10m
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
https://github.com/my-yy/learnable_pins
https://github.com/mzamini92/preprocessed-cord-19
https://github.com/nahidalam/open_clip
Reproducible scaling laws for contrastive language-image learning
https://github.com/nalexai/hyperlib
https://github.com/namakemono/kaggle-alaska2-image-steganalysis
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
https://github.com/namtuanly/MTL-TabNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.