https://github.com/acharkq/prott3
공식 코드ProtT3: Protein-to-Text Generation for Text-based Protein Understanding
Papers with Code 아카이브
Text Retrieval
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Text Retrieval is the task of finding the most text result (such as an answer, paragraph, or passage) given a query (which could be a question, keywords, or any relevant text)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/akoepke/audio-retrieval-benchmark
공식 코드Audio Retrieval with Natural Language Queries: A Benchmark Study
https://github.com/ale9806/open_clip_with_biomedica
공식 코드https://github.com/alevas/xai_similarity_transformers
공식 코드https://github.com/alfassy/FETA
공식 코드FETA: Towards Specializing Foundation Models for Expert Task Applications
https://github.com/algoprog/ProCIS
공식 코드ProCIS: A Benchmark for Proactive Retrieval in Conversations
https://github.com/alibaba/AliceMind/tree/main/mPLUG
공식 코드mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
https://github.com/alinlab/s-clip
공식 코드S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
https://github.com/alipay/Ant-Multi-Modal-Framework
공식 코드https://github.com/alirezamshi/AME-CMR
공식 코드Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task
https://github.com/alisafaya/neurocache
공식 코드Neurocache: Efficient Vector Retrieval for Long-range Language Modeling
https://github.com/alisonsneyd/poisson_stopping_method
공식 코드Modelling Stopping Criteria for Search Results using Poisson Processes
https://github.com/amazon-research/mix-generation
공식 코드https://github.com/amy-hyunji/Generative-Multihop-Retrieval
공식 코드https://github.com/amy-hyunji/contextualized-generative-retrieval
공식 코드https://github.com/amzn/pecos
공식 코드PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models
https://github.com/andreeaiana/nase
공식 코드https://github.com/antoine77340/Mixture-of-Embedding-Experts
공식 코드Learning a Text-Video Embedding from Incomplete and Heterogeneous Data
https://github.com/apple/ml-veclip
공식 코드VeCLIP: Improving CLIP Training via Visual-enriched Captions
https://github.com/arthur422tp/hierachical
공식 코드https://github.com/asash/shallow-cross-encoders
공식 코드https://github.com/asyml/forte
공식 코드https://github.com/atosystem/speechclip
공식 코드SpeechCLIP: Integrating Speech with Pre-Trained Vision and Language Model
https://github.com/atri-acl/atri-acl
공식 코드https://github.com/awslabs/decode-answer-logical-form
공식 코드DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases
https://github.com/baai-dcai/m3d
공식 코드M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
https://github.com/buaadreamer/ccrk
공식 코드Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning
https://github.com/canonchen/imc
공식 코드https://github.com/caryxiang/eca4vtr
공식 코드Expertized Caption Auto-Enhancement for Video-Text Retrieval
https://github.com/castorini/dhr
공식 코드A Dense Representation Framework for Lexical and Semantic Matching
https://github.com/castorini/pyserini
공식 코드https://github.com/cgcl-codes/advclip
공식 코드AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning
https://github.com/chao1224/moleculestm
공식 코드Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
https://github.com/chendelong1999/remoteclip
공식 코드RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
https://github.com/chinmayad/motiondescriptions
공식 코드Diving Deep into the Motion Representation of Video-Text Models
https://github.com/chuhanxx/helping_hand_for_egocentric_videos
공식 코드Helping Hands: An Object-Aware Ego-Centric Video Recognition Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.