https://github.com/muktac5/ciir-liverag
공식 코드CIIR@LiveRAG 2025: Optimizing Multi-Agent Retrieval Augmented Generation through Self-Training
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/mul-hjh/acsa
공식 코드Asymmetric Cross-Scale Alignment for Text-Based Person Search
https://github.com/mulab-mir/song-describer-dataset
공식 코드The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation
https://github.com/muleina/lidd
공식 코드Lightweight Multi-System Multivariate Interconnection and Divergence Discovery
https://github.com/multimodal-interpretability/nnn
공식 코드Nearest Neighbor Normalization Improves Multimodal Retrieval
https://github.com/music-classification/tutorial
공식 코드Music Classification: Beyond Supervised Learning, Towards Real-world Applications
https://github.com/music-x-lab/hierarchical-metrical-structure
공식 코드https://github.com/musicman217/Text-Proxy
공식 코드https://github.com/musicman217/gare-text-video-retrieval
공식 코드Contrastive Alignment with Semantic Gap-Aware Corrections in Text-Video Retrieval
https://github.com/mvp18/KD-SVD
공식 코드https://github.com/mvp18/UCDR
공식 코드Universal Cross-Domain Retrieval: Generalizing Across Classes and Domains
https://github.com/mvp18/ttt-ucdr
공식 코드https://github.com/mvrl/birdsat
공식 코드BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping
https://github.com/mvrl/context-cir
공식 코드ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval
https://github.com/mwray/Multi-Verb-Labels
공식 코드https://github.com/mwray/Semantic-Video-Retrieval
공식 코드https://github.com/mxpoliakov/multi-meta-rag
공식 코드https://github.com/myavartanoo/PolyNet_PyTorch
공식 코드PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation
https://github.com/myeon9h/planrag
공식 코드https://github.com/myfoodchoice/malm_mask_augmentation_based_local_matching-_for-_food_recipe_retrieval
공식 코드MALM: Mask Augmentation based Local Matching for Food-Recipe Retrieval
https://github.com/mysong7nlper/hypermatch
공식 코드Hyperbolic Relevance Matching for Neural Keyphrase Extraction
https://github.com/mzhang367/dcwh
공식 코드Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise Loss
https://github.com/mzhang367/hpdh
공식 코드Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval
https://github.com/mzhang367/opqn
공식 코드Orthonormal Product Quantization Network for Scalable Face Image Retrieval
https://github.com/mzhaoshuai/CenterCLIP
공식 코드CenterCLIP: Token Clustering for Efficient Text-Video Retrieval
https://github.com/mzhaoshuai/rlcf
공식 코드Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models
https://github.com/mzolfaghari/ECO-efficient-video-understanding
공식 코드ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/n-nejatishahidin/pose_from_mid-level
공식 코드Object Pose Estimation using Mid-level Visual Representations
https://github.com/n4feng/ResponseQualityAssessment
공식 코드Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
https://github.com/nadavsc/diff-mamba
공식 코드https://github.com/nainye/radiomicsretrieval
공식 코드RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features
https://github.com/naiveneuron/bryndza-case-2024
공식 코드https://github.com/namespace-pt/adon
공식 코드Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval
https://github.com/namespace-pt/tsgen
공식 코드https://github.com/nannanli999/airr
공식 코드Supervised Attribute Information Removal and Reconstruction for Image Manipulation
https://github.com/narabzad/genir-evaluation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.