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/salesforce/lavis
공식 코드Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
https://github.com/salesforce/qvr-simpledlm
공식 코드Value Retrieval with Arbitrary Queries for Form-like Documents
https://github.com/salesforceairesearch/faitheval
공식 코드https://github.com/sam-rag/sam_rag
공식 코드https://github.com/samarth4149/PAN
공식 코드https://github.com/samarth4149/syncdr
공식 코드SynCDR : Training Cross Domain Retrieval Models with Synthetic Data
https://github.com/samblouir/birdie
공식 코드Birdie: Advancing State Space Models with Reward-Driven Objectives and Curricula
https://github.com/samehfrihat/LevelOfEvidence
공식 코드Supporting Evidence-Based Medicine by Finding Both Relevant and Significant Works
https://github.com/samia067/crisp
공식 코드CriSp: Leveraging Tread Depth Maps for Enhanced Crime-Scene Shoeprint Matching
https://github.com/samtalki/powerphaseretrieval.jl
공식 코드https://github.com/samyateia/bioasq
공식 코드https://github.com/sanderwood/clamp2
공식 코드CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models
https://github.com/sanxing-chen/adv-fake
공식 코드https://github.com/sapienzanlp/zebra
공식 코드ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering
https://github.com/saraabdollahi/laser
공식 코드https://github.com/saraghazanfari/lipsim
공식 코드https://github.com/sarashs/fpga_agi
공식 코드SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation
https://github.com/sarkar-rohan/objectswithstatechange
공식 코드A Dataset and Framework for Learning State-invariant Object Representations
https://github.com/sarthmit/compositional-attention
공식 코드https://github.com/sarus-tech/dp-rag
공식 코드https://github.com/satoru2001/cukdfir
공식 코드https://github.com/satvik-venkatesh/you-only-hear-once
공식 코드You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
https://github.com/satya77/Entity_Embedding
공식 코드https://github.com/satya77/Transformer_Temporal_Tagger
공식 코드BERT got a Date: Introducing Transformers to Temporal Tagging
https://github.com/satya77/quantityawarerankers
공식 코드Numbers Matter! Bringing Quantity-awareness to Retrieval Systems
https://github.com/savasozkan/i2v
공식 코드Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-to-Video Search
https://github.com/sayaendo/document-set-expansion-pu
공식 코드Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning
https://github.com/sayannath/identical-image-retrieval
공식 코드https://github.com/sayazhang/spatial-query-by-sketch
공식 코드DeepSSN: a deep convolutional neural network to assess spatial scene similarity
https://github.com/sch971111/mof
공식 코드Efficient Cross-Modal Video Retrieval with Meta-Optimized Frames
https://github.com/scientific-computing-nrcn/SimulAI
공식 코드Complete CVDL Methodology for Investigating Hydrodynamic Instabilities
https://github.com/scitix/MEAP
공식 코드Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn More
https://github.com/sciyu/kae
공식 코드KAE: Kolmogorov-Arnold Auto-Encoder for Representation Learning
https://github.com/scottgeng00/unmet-promise
공식 코드The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better
https://github.com/scut-dlvclab/dolphin
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.