Papers with Code 아카이브
Retrieval
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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/stanfordnlp/dsp
공식 코드Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
https://github.com/stanfordnlp/mixed-distribution-retrieval
공식 코드Resources and Evaluations for Multi-Distribution Dense Information Retrieval
https://github.com/stanis-morozov/rpg
공식 코드https://github.com/stanis-morozov/unq
공식 코드Unsupervised Neural Quantization for Compressed-Domain Similarity Search
https://github.com/stanlei52/aqvsr
공식 코드AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant
https://github.com/stanlei52/tqvsr
공식 코드AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant
https://github.com/stanliu96/RATD
공식 코드Retrieval-Augmented Diffusion Models for Time Series Forecasting
https://github.com/staoxiao/libvq
공식 코드https://github.com/staoxiao/retromae
공식 코드RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder
https://github.com/starlitlog/dr-web-engine
공식 코드https://github.com/starmemda/CAMoE
공식 코드Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
https://github.com/starmemda/camow
공식 코드Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
https://github.com/starmpcc/remed
공식 코드General-Purpose Retrieval-Enhanced Medical Prediction Model Using Near-Infinite History
https://github.com/starsuzi/adaptive-rag
공식 코드https://github.com/starsuzi/dar
공식 코드Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation
https://github.com/starsuzi/pro-convqa
공식 코드https://github.com/starsuzi/udeg
공식 코드Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation
https://github.com/starsuzi/videorag
공식 코드https://github.com/starxliu/FDDH
공식 코드FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal Retrieval
https://github.com/starxliu/MTFH
공식 코드MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval
https://github.com/stat-ml/face-evaluation
공식 코드https://github.com/stechlab/visual-near-duplicates-detection-in-the-context-of-social-media
공식 코드Dataset and Case Studies for Visual Near-Duplicates Detection in the Context of Social Media
https://github.com/stefan-ainetter/SCANnotatepp
공식 코드Leveraging Automatic CAD Annotations for Supervised Learning in 3D Scene Understanding
https://github.com/stella-project/stella-evaluations
공식 코드https://github.com/sthalham/tram3d
공식 코드Self-supervised Vision Transformers for 3D Pose Estimation of Novel Objects
https://github.com/sthalles/carp
공식 코드Representation Learning via Consistent Assignment of Views over Random Partitions
https://github.com/sthalles/massl
공식 코드Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features
https://github.com/stogiannidis/srbench
공식 코드Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models
https://github.com/stonybrooknlp/ircot
공식 코드Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
https://github.com/studio-ousia/bpr
공식 코드Efficient Passage Retrieval with Hashing for Open-domain Question Answering
https://github.com/subhadeepkoley/demosketch2rgb
공식 코드It's All About Your Sketch: Democratising Sketch Control in Diffusion Models
https://github.com/subhc/clever
공식 코드The Curious Layperson: Fine-Grained Image Recognition without Expert Labels
https://github.com/successar/instance_attributions_NLP
공식 코드An Empirical Comparison of Instance Attribution Methods for NLP
https://github.com/successar/neural-nlp
공식 코드Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
https://github.com/sudo-Boris/mr-Blip
공식 코드The Surprising Effectiveness of Multimodal Large Language Models for Video Moment Retrieval
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