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/puar-playground/lra-diffusion
공식 코드Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
https://github.com/puneesh00/loop
공식 코드LoOp: Looking for Optimal Hard Negative Embeddings for Deep Metric Learning
https://github.com/pxiangwu/forb
공식 코드FORB: A Flat Object Retrieval Benchmark for Universal Image Embedding
https://github.com/pxyu/csv-for-lsr-ecir24
공식 코드Improved Learned Sparse Retrieval with Corpus-Specific Vocabularies
https://github.com/pytorch/fairseq
공식 코드VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding
https://github.com/pytorch/fairseq/tree/master/examples/criss
공식 코드Cross-lingual Retrieval for Iterative Self-Supervised Training
https://github.com/qhjqhj00/cikm2021-impchat
공식 코드Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot
https://github.com/qhjqhj00/inforage
공식 코드Scent of Knowledge: Optimizing Search-Enhanced Reasoning with Information Foraging
https://github.com/qhjqhj00/memorag
공식 코드MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery
https://github.com/qhjqhj00/webbrain
공식 코드https://github.com/qiaoyf96/2gti
공식 코드Optimizing Guided Traversal for Fast Learned Sparse Retrieval
https://github.com/qiaoyf96/ht
공식 코드Representation Sparsification with Hybrid Thresholding for Fast SPLADE-based Document Retrieval
https://github.com/qinchuanhui/uda-benchmark
공식 코드UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document Analysis
https://github.com/qingchen239/ld-detr
공식 코드LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval and Highlight Detection
https://github.com/qingfei1/longrag
공식 코드https://github.com/qinjr/UBR4CTR
공식 코드https://github.com/qinjr/rim
공식 코드https://github.com/qinnzou/Zero-Shot-Hashing
공식 코드Transductive Zero-Shot Hashing for Multilabel Image Retrieval
https://github.com/qinyuma316/retrosynthesisagent
공식 코드Leveraging Large Language Models as Knowledge-Driven Agents for Reliable Retrosynthesis Planning
https://github.com/qipeng/golden-retriever
공식 코드Answering Complex Open-domain Questions Through Iterative Query Generation
https://github.com/qisun123/genrdk
공식 코드https://github.com/qiujiali/lattice_rnn
공식 코드Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation
https://github.com/qiustander/siprnet
공식 코드SiSPRNet: End-to-End Learning for Single-Shot Phase Retrieval
https://github.com/qiuzh20/hma
공식 코드https://github.com/qiuzx2/micpq
공식 코드https://github.com/qizaowang/fire-ccreid
공식 코드Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
https://github.com/qizhou000/RECIPE
공식 코드Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning
https://github.com/qliu24/SAKE
공식 코드Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval
https://github.com/qqw-ing/rag-reasonalignment
공식 코드AlignRAG: Leveraging Critique Learning for Evidence-Sensitive Retrieval-Augmented Reasoning
https://github.com/qshi95/lergv
공식 코드https://github.com/qtacierp/prior
공식 코드PRIOR: Prototype Representation Joint Learning from Medical Images and Reports
https://github.com/quangtrungtruong/KIS_MVK
공식 코드Marine Video Kit: A New Marine Video Dataset for Content-based Analysis and Retrieval
https://github.com/quchangle1/colt
공식 코드Towards Completeness-Oriented Tool Retrieval for Large Language Models
https://github.com/quqxui/memgas
공식 코드Towards Multi-Granularity Memory Association and Selection for Long-Term Conversational Agents
https://github.com/qwenlm/qwen3-embedding
공식 코드Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.