Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/oliverrose1998/Attention-Confidence
Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation
https://github.com/omrijsharon/torchlex
https://github.com/open-compass/CIBench
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step
https://github.com/open-mmlab/mmpose
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
https://github.com/opengvlab/cafo
Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification
https://github.com/opengvlab/internvl-mmdetseg
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
https://github.com/opennlg/openba-v2
https://github.com/opscidia/science-checker
Science Checker Reloaded: A Bidirectional Paradigm for Transparency and Logical Reasoning
https://github.com/optyang/BSCA
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization
https://github.com/orion-ai-lab/eouncertaintygeneralization
https://github.com/osirrc/nvsm-docker
https://github.com/osirrc2019/nvsm-docker
https://github.com/oskyhn/CNNs-Without-Borders
Working hard to know your neighbor's margins: Local descriptor learning loss
https://github.com/owennju/mmt-llm
https://github.com/owj0421/OutfitTransformer
OutfitTransformer: Learning Outfit Representations for Fashion Recommendation
https://github.com/owj0421/csa-net
https://github.com/owj0421/outfit-transformer
OutfitTransformer: Learning Outfit Representations for Fashion Recommendation
https://github.com/pandigreat/DELF
Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/paper-submission-coder/seminar
Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction
https://github.com/parkererickson/crunchBaseGraph
https://github.com/parry-parry/pyterrier_t5
Declarative Experimentation in Information Retrieval using PyTerrier
https://github.com/paschalidoud/superquadric_parsing
3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks
https://github.com/pefimov/cross-lingual-adjustment
https://github.com/peizeli96/MHys
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering
https://github.com/pertusa/multilabelLogoRecognition
Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features
https://github.com/phamnam-mta/ConveRT-PolyAI-Vietnamese
ConveRT: Efficient and Accurate Conversational Representations from Transformers
https://github.com/pharmolix/openbiomed
https://github.com/phbst/tinyrag
https://github.com/phenixace/molregpt
https://github.com/philippe-gagne/treasure-boxd
Deep Learning based Recommender System: A Survey and New Perspectives
https://github.com/phunterlau/paper_without_code/tree/main/examples/community-rag
https://github.com/pinecone-io/pinecone-text
SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval
https://github.com/pku-yuangroup/open-sora-plan
Extending Context Window of Large Language Models via Positional Interpolation
https://github.com/pku-yuangroup/video-bench
https://github.com/popcornell/keras-triplet-center-loss
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.