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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/llm-lab-org/CLASP
공식 코드CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information Retrieval
https://github.com/lluisgomez/single-shot-str
공식 코드https://github.com/longkukuhi/multiway-adapter
공식 코드MultiWay-Adapater: Adapting large-scale multi-modal models for scalable image-text retrieval
https://github.com/louisdo/eru-kg
공식 코드ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation
https://github.com/lsh0520/3d-molm
공식 코드https://github.com/lttsmn/LXMERT-S
공식 코드Multi-stage Pre-training over Simplified Multimodal Pre-training Models
https://github.com/lunarshen/dsicovla
공식 코드https://github.com/luyug/Condenser
공식 코드https://github.com/luyug/Reranker
공식 코드Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline
https://github.com/lyq312318224/mllms-augmented
공식 코드https://github.com/m-bain/frozen-in-time
공식 코드Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
https://github.com/m2man/HADA-LAVIS
공식 코드HADA: A Graph-based Amalgamation Framework in Image-text Retrieval
https://github.com/m2man/LGSGM
공식 코드A Deep Local and Global Scene-Graph Matching for Image-Text Retrieval
https://github.com/magiccircuit/enhance-finegrained
공식 코드https://github.com/matanbt/gaslite
공식 코드GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based Search
https://github.com/mcahny/rovit
공식 코드Region-Aware Pretraining for Open-Vocabulary Object Detection with Vision Transformers
https://github.com/mcr-peft/ex-mcr
공식 코드https://github.com/mczhuge/Kaleido-BERT
공식 코드Kaleido-BERT: Vision-Language Pre-training on Fashion Domain
https://github.com/miccunifi/cross-the-gap
공식 코드Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion
https://github.com/microsoft/ANCE
공식 코드Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
https://github.com/microsoft/ar2
공식 코드https://github.com/microsoft/fiber
공식 코드Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone
https://github.com/microsoft/simxns
공식 코드SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval
https://github.com/microsoft/unilm/tree/master/vlmo
공식 코드VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
https://github.com/microsoft/wavtext5k
공식 코드https://github.com/microsoft/xpretrain
공식 코드CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment
https://github.com/mikelwl/fico-itr
공식 코드https://github.com/minogame/clip-mtob
공식 코드Design of the topology for contrastive visual-textual alignment
https://github.com/minwoosun/biomedica-etl
공식 코드https://github.com/mirthai/med3dvlm
공식 코드Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis
https://github.com/mlvlab/vid-tldr
공식 코드vid-TLDR: Training Free Token merging for Light-weight Video Transformer
https://github.com/modelscope/adaseq
공식 코드Named Entity and Relation Extraction with Multi-Modal Retrieval
https://github.com/momarky/egma
공식 코드Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning
https://github.com/mshukor/vicha
공식 코드Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment
https://github.com/mugen-org/MUGEN_baseline
공식 코드MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.