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/swj0419/knn_prompt
공식 코드https://github.com/swordlidev/LLaVA-MR
공식 코드LLaVA-MR: Large Language-and-Vision Assistant for Video Moment Retrieval
https://github.com/swyoon/aaai2021-scene-graph-img-retr
공식 코드Image-to-Image Retrieval by Learning Similarity between Scene Graphs
https://github.com/sxjyjay/tv-net
공식 코드Two-stage Visual Cues Enhancement Network for Referring Image Segmentation
https://github.com/sycny/rae
공식 코드Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering
https://github.com/syed0093-umn/SemEval_Task7
공식 코드https://github.com/sylphai-inc/adalflow
공식 코드https://github.com/syqlyx/StructVPR
공식 코드https://github.com/syr-cn/autorefine
공식 코드Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs
https://github.com/szeighami/nudge
공식 코드NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval
https://github.com/szhang42/Calibration_qa
공식 코드Knowing More About Questions Can Help: Improving Calibration in Question Answering
https://github.com/tLabruna/Adapt-LLM
공식 코드When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively
https://github.com/taco-group/re-align
공식 코드Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization
https://github.com/taesunwhang/UMS-ResSel
공식 코드https://github.com/taewhankim/vipcap
공식 코드ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image Captioning
https://github.com/takahikof/deepdiffusion
공식 코드https://github.com/tamsiuhin/oppu
공식 코드Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning
https://github.com/tanay2001/core
공식 코드CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation
https://github.com/tanchongmin/learning-fast-and-slow
공식 코드Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments
https://github.com/tanghaoyu258/ACRM-for-moment-retrieval
공식 코드https://github.com/tanghaoyu258/feel
공식 코드Visual Self-paced Iterative Learning for Unsupervised Temporal Action Localization
https://github.com/tangjianpku/LINE
공식 코드Name Disambiguation in Anonymized Graphs using Network Embedding
https://github.com/tangqiaoyu/selfretrieval
공식 코드Self-Retrieval: End-to-End Information Retrieval with One Large Language Model
https://github.com/tangzhy/dptdr
공식 코드https://github.com/tanqiujiang/rapid
공식 코드RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models
https://github.com/tanveer81/rgnet
공식 코드RGNet: A Unified Clip Retrieval and Grounding Network for Long Videos
https://github.com/taolei87/rcnn
공식 코드https://github.com/taoshen58/lexmae
공식 코드LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval
https://github.com/tasosnent/BeyondMeSH
공식 코드Beyond MeSH: Fine-Grained Semantic Indexing of Biomedical Literature based on Weak Supervision
https://github.com/tasosnent/Biomedical-Knowledge-Integration
공식 코드iASiS Open Data Graph: Automated Semantic Integration of Disease-Specific Knowledge
https://github.com/tavisshore/BEV-CV
공식 코드BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation
https://github.com/tavisshore/PEnG
공식 코드https://github.com/tavisshore/SpaGBOL
공식 코드https://github.com/tayebiarasteh/radiorag
공식 코드https://github.com/tbijl/course_ranking_data
공식 코드Efficient course recommendations with T5-based ranking and summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.