https://github.com/bladewaltz1/promptswitch
공식 코드Prompt Switch: Efficient CLIP Adaptation for Text-Video Retrieval
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/blbadger/maskedmixers
공식 코드https://github.com/blender-nlp/entityconditionedqgen
공식 코드https://github.com/bmc-sdnu/cross-modal-retrieval
공식 코드Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
https://github.com/bmezaris/texttovideoretrieval-ttimesv
공식 코드https://github.com/bmitra-msft/Luandri
공식 코드https://github.com/bmitra-msft/TREC-Deep-Learning-Quick-Start
공식 코드Conformer-Kernel with Query Term Independence for Document Retrieval
https://github.com/bofang98/uatvr
공식 코드https://github.com/bohanhou14/clerc
공식 코드CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation
https://github.com/bojone/BERT-whitening
공식 코드Whitening Sentence Representations for Better Semantics and Faster Retrieval
https://github.com/booydar/t5-experiments
공식 코드https://github.com/boreng0817/ifcap
공식 코드IFCap: Image-like Retrieval and Frequency-based Entity Filtering for Zero-shot Captioning
https://github.com/boschresearch/rince
공식 코드Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives
https://github.com/bostxavier/Serial-Speakers
공식 코드https://github.com/boudinfl/ir-using-kg
공식 코드https://github.com/bowang-lab/joint-ner-and-re
공식 코드End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models
https://github.com/brandonhanx/compfashion
공식 코드https://github.com/brandonhanx/fame-vil
공식 코드FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks
https://github.com/brandonhanx/mmf
공식 코드FashionViL: Fashion-Focused Vision-and-Language Representation Learning
https://github.com/brandonhanx/textreid
공식 코드https://github.com/breuert/sigirap24
공식 코드Data Fusion of Synthetic Query Variants With Generative Large Language Models
https://github.com/brian7685/Multimodal-Clustering-Network
공식 코드Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos
https://github.com/brmson/dataset-sts
공식 코드Joint Learning of Sentence Embeddings for Relevance and Entailment
https://github.com/brochier/impact_query_expert_finding
공식 코드Impact of the Query Set on the Evaluation of Expert Finding Systems
https://github.com/brown-palm/ObjectPrompt
공식 코드Object-centric Video Representation for Long-term Action Anticipation
https://github.com/brucewang123456789/GeniusTrail
공식 코드Wormhole Memory: A Rubik's Cube for Cross-Dialogue Retrieval
https://github.com/bryant-nn/template-based-financial-report-generation
공식 코드Template-Based Financial Report Generation in Agentic and Decomposed Information Retrieval
https://github.com/bryant1410/fitclip
공식 코드FitCLIP: Refining Large-Scale Pretrained Image-Text Models for Zero-Shot Video Understanding Tasks
https://github.com/bryant1410/tclip
공식 코드FitCLIP: Refining Large-Scale Pretrained Image-Text Models for Zero-Shot Video Understanding Tasks
https://github.com/bsinghpratap/scan
공식 코드https://github.com/buaadreamer/ccrk
공식 코드Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning
https://github.com/buaadreamer/easyrag
공식 코드EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations
https://github.com/budnikm/aml
공식 코드https://github.com/bui1dmysea/memlong
공식 코드https://github.com/bunny-black/prunnet
공식 코드Learning Compatible Multi-Prize Subnetworks for Asymmetric Retrieval
https://github.com/bunsenfeng/abstainqa
공식 코드Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.