https://github.com/AnjanDutta/sem-pcyc
공식 코드Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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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/AnnikaLindh/Diverse_and_Specific_Image_Captioning
공식 코드https://github.com/AnshanTJU/FILD
공식 코드Fast and Incremental Loop Closure Detection Using Proximity Graphs
https://github.com/Anup-Deshmukh/TREC_background_linking
공식 코드IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles
https://github.com/Applied-Machine-Learning-Lab/Awesome-Personalized-RAG-Agent
공식 코드https://github.com/Ariya12138/CORAL
공식 코드CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation
https://github.com/ArminS03/CAREL
공식 코드CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives
https://github.com/ArrowLuo/CLIP4Clip
공식 코드CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
https://github.com/Arstanley/Awesome-Trustworthy-Retrieval-Augmented-Generation
공식 코드Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
https://github.com/ArthurSBianchessi/BAM
공식 코드https://github.com/AskNowQA/DeepShallowParsingQA
공식 코드https://github.com/Atipico1/robust-rag
공식 코드https://github.com/Atmegal/DGCPN
공식 코드Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal Hashing
https://github.com/Atomu2014/product-nets-distributed
공식 코드Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
https://github.com/AutoAILab/M2CP-Learning
공식 코드https://github.com/AutoVision-cloud/SSL-ViT-lowlabel-highdata
공식 코드Visual Representation Learning with Self-Supervised Attention for Low-Label High-data Regime
https://github.com/Aveek-Saha/VIRALIQ
공식 코드https://github.com/AyanKumarBhunia/Deep-One-Shot-Logo-Retrieval
공식 코드https://github.com/AyyerLab/RefSPI-MaxLP
공식 코드Holographic single particle imaging for weakly scattering, heterogeneous nanoscale objects
https://github.com/BIT-DYN/OpenObj
공식 코드OpenObj: Open-Vocabulary Object-Level Neural Radiance Fields with Fine-Grained Understanding
https://github.com/BUAADreamer/SPN4CIR
공식 코드Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives
https://github.com/BananaTonic/Material_Synthesis_Corpus
공식 코드Analyzing Research Trends in Inorganic Materials Literature Using NLP
https://github.com/BeastyZ/ConvSearch-R1
공식 코드https://github.com/Ben-Louis/SBIR-DASE-MEMS
공식 코드https://github.com/BerenMillidge/Theory_Associative_Memory
공식 코드Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models
https://github.com/BestJuly/Inter-intra-video-contrastive-learning
공식 코드Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
https://github.com/Blubberli/perspective-argument-retrieval
공식 코드Overview of PerpectiveArg2024: The First Shared Task on Perspective Argument Retrieval
https://github.com/BorealisAI/Autocast-plus-plus
공식 코드AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
https://github.com/BruceW91/CVSE
공식 코드Consensus-Aware Visual-Semantic Embedding for Image-Text Matching
https://github.com/C4AI/Pira
공식 코드Pirá: A Bilingual Portuguese-English Dataset for Question-Answering about the Ocean
https://github.com/CASIA-IVA-Lab/ISP-reID
공식 코드Identity-Guided Human Semantic Parsing for Person Re-Identification
https://github.com/CGCL-codes/naturalcc
공식 코드https://github.com/COR-OPT/GeomStepDecay
공식 코드Stochastic algorithms with geometric step decay converge linearly on sharp functions
https://github.com/CPJKU/DeepGenIR
공식 코드A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
https://github.com/CPJKU/audioLIME
공식 코드https://github.com/CPJKU/cca_layer
공식 코드End-to-End Cross-Modality Retrieval with CCA Projections and Pairwise Ranking Loss
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.