https://github.com/ocean-luna/hmrag
공식 코드HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 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/oceanntwt/ra-isf
공식 코드RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
https://github.com/ofa-sys/chinese-clip
공식 코드Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
https://github.com/offry/arc-aid
공식 코드https://github.com/ogaloglu/pre-training-multilingual-document-encoders
공식 코드https://github.com/ohadrubin/rpt
공식 코드Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrieval
https://github.com/ojrlopez27/nl-service-composition
공식 코드NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings
https://github.com/oldaandozerskaya/age_based_classification
공식 코드A Comparative Study of Feature Types for Age-Based Text Classification
https://github.com/oliversavolainen/axiomatic-ir-reproduce
공식 코드https://github.com/olivettigroup/cross-domain-exploration
공식 코드Augmenting Scientific Creativity with Retrieval across Knowledge Domains
https://github.com/om-ai-lab/imagerag
공식 코드ImageRAG: Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with ImageRAG
https://github.com/om-ai-lab/rs5m
공식 코드https://github.com/omkarthawakar/composed-video-retrieval
공식 코드Composed Video Retrieval via Enriched Context and Discriminative Embeddings
https://github.com/omron-sinicx/com_kitchens
공식 코드COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language Benchmark
https://github.com/oncescuandreea/audio-retrieval
공식 코드https://github.com/oneal2000/caseformer
공식 코드Caseformer: Pre-training for Legal Case Retrieval Based on Inter-Case Distinctions
https://github.com/oneal2000/dragin
공식 코드https://github.com/oneal2000/entityhallucination
공식 코드Mitigating Entity-Level Hallucination in Large Language Models
https://github.com/oneal2000/prag
공식 코드https://github.com/oneal2000/stard
공식 코드STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals
https://github.com/oneal2000/wikiformer
공식 코드Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrieval
https://github.com/onlinehuazai/evit
공식 코드EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing
https://github.com/onlplab/hegel
공식 코드https://github.com/open-compass/opencompass
공식 코드NeedleBench: Can LLMs Do Retrieval and Reasoning in Information-Dense Context?
https://github.com/open-compass/t-eval
공식 코드T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step
https://github.com/open-dataflow/rare
공식 코드https://github.com/open-event-hub/event-enhanced_retrieval
공식 코드https://github.com/open-mmlab/mmediting
공식 코드https://github.com/open-mmlab/mmfashion
공식 코드MMFashion: An Open-Source Toolbox for Visual Fashion Analysis
https://github.com/openbmb/debater
공식 코드https://github.com/openbmb/rageval
공식 코드RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
https://github.com/openbmb/ultrarag
공식 코드UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation
https://github.com/openbmb/visrag
공식 코드VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents
https://github.com/opendatalab/OHR-Bench
공식 코드OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation
https://github.com/opengvlab/all-seeing
공식 코드The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World
https://github.com/opengvlab/internvideo
공식 코드InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.