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/videocrafter/animate-a-story
공식 코드Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation
https://github.com/vikas95/AIR-retriever
공식 코드Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering
https://github.com/vikas95/Pref_Suff_Span_NN
공식 코드A Survey on Recent Advances in Named Entity Recognition from Deep Learning models
https://github.com/vimar-gu/msinet
공식 코드MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID
https://github.com/vimpire00/know2vec1
공식 코드https://github.com/vinairesearch/whoqa
공식 코드Who's Who: Large Language Models Meet Knowledge Conflicts in Practice
https://github.com/vinsontang1/slidecoder
공식 코드SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design
https://github.com/violet24k/sphere
공식 코드SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval
https://github.com/vishaal27/sus-x
공식 코드SuS-X: Training-Free Name-Only Transfer of Vision-Language Models
https://github.com/vision-cair/dochaystacks
공식 코드Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents
https://github.com/visionjo/pykinship
공식 코드Recognizing Families In the Wild: White Paper for the 4th Edition Data Challenge
https://github.com/vistec-ai/nitibench
공식 코드NitiBench: A Comprehensive Studies of LLM Frameworks Capabilities for Thai Legal Question Answering
https://github.com/visual-computing/las_flas
공식 코드https://github.com/visual-haystacks/vhs_benchmark
공식 코드Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark
https://github.com/visualaikhu/keyword-detr
공식 코드https://github.com/viswavi/datafinder
공식 코드DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions
https://github.com/vita-group/llm-kick
공식 코드https://github.com/vkhoi/cora_cvpr24
공식 코드Composing Object Relations and Attributes for Image-Text Matching
https://github.com/vl2g/cofar
공식 코드https://github.com/vlievin/medical-reasoning
공식 코드https://github.com/vlsomers/bpbreid
공식 코드Body Part-Based Representation Learning for Occluded Person Re-Identification
https://github.com/vntkumar8/musical-instrument-classification
공식 코드Predominant Musical Instrument Classification based on Spectral Features
https://github.com/voidism/ear
공식 코드Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering
https://github.com/vsd-benchmark/vsd
공식 코드Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond
https://github.com/vvvwo/kss-icp
공식 코드KSS-ICP: Point Cloud Registration based on Kendall Shape Space
https://github.com/waetr/KET-RAG
공식 코드KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG
https://github.com/waldenruc/ufo
공식 코드UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models
https://github.com/wallace-chen/tbh
공식 코드https://github.com/waltersimoncini/fungivision
공식 코드No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations
https://github.com/wang-ml-lab/multimodal-needle-in-a-haystack
공식 코드https://github.com/wangchangjen/gec-sr-pr-hypernets
공식 코드Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork
https://github.com/wangck20/idml
공식 코드https://github.com/wangcunxiang/llm-factuality-survey
공식 코드Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
https://github.com/wangfei-2019/image-text-retrieval
공식 코드https://github.com/wangjw6/llmob
공식 코드Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
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