https://github.com/ikuinen/regularized_two-branch_proposal_network
공식 코드Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/ilaria-manco/mulap
공식 코드Learning music audio representations via weak language supervision
https://github.com/ilaria-manco/muscall
공식 코드https://github.com/ilaria-manco/muscaps
공식 코드https://github.com/illuin-tech/contextual-embeddings
공식 코드Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings
https://github.com/illuin-tech/vidore-benchmark
공식 코드https://github.com/imatge-upc/egocentric-2017-lta
공식 코드https://github.com/imatge-upc/retrieval-2016-deepvision
공식 코드https://github.com/imatge-upc/retrieval-2016-icmr
공식 코드Bags of Local Convolutional Features for Scalable Instance Search
https://github.com/imatge-upc/retrieval-2017-cam
공식 코드Class-Weighted Convolutional Features for Visual Instance Search
https://github.com/imayk/crush4sql
공식 코드CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL
https://github.com/imccretrieval/momentdiff
공식 코드MomentDiff: Generative Video Moment Retrieval from Random to Real
https://github.com/imirandam/bivlc
공식 코드BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval
https://github.com/imirandam/structure-inference-compositionality
공식 코드Adding simple structure at inference improves Vision-Language Compositionality
https://github.com/imperialite/standardize-ctg
공식 코드Standardize: Aligning Language Models with Expert-Defined Standards for Content Generation
https://github.com/indic-aidias/mst-r
공식 코드MST-R: Multi-Stage Tuning for Retrieval Systems and Metric Evaluation
https://github.com/info-regularization/information-regularization
공식 코드https://github.com/informagi/GEEER
공식 코드https://github.com/informagi/conversational-entity-linking
공식 코드Conversational Entity Linking: Problem Definition and Datasets
https://github.com/informagi/embert
공식 코드https://github.com/init0xyz/AdaCQR
공식 코드https://github.com/inquire-benchmark/INQUIRE
공식 코드https://github.com/intellabs/multimodal_cognitive_ai
공식 코드ICSVR: Investigating Compositional and Syntactic Understanding in Video Retrieval Models
https://github.com/intellabs/rag-retrieval-study
공식 코드https://github.com/intellisensing/rs-agent
공식 코드RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent
https://github.com/intellistream/candy
공식 코드CANDY: A Benchmark for Continuous Approximate Nearest Neighbor Search with Dynamic Data Ingestion
https://github.com/interestingzhuo/pnp_loss
공식 코드https://github.com/interestingzhuo/pnploss
공식 코드https://github.com/internlm/swe-fixer
공식 코드SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution
https://github.com/intersun/LightningDOT
공식 코드LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval
https://github.com/invictus717/MiCo
공식 코드https://github.com/ioanacroi/qb-norm
공식 코드https://github.com/ioannis-papadimitriou/rag-playground
공식 코드https://github.com/ipouyall/can-llms-be-lateral-thinkers
공식 코드uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?
https://github.com/ireinman/the-search-for-stability
공식 코드The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents
https://github.com/irgroup/Reanimator
공식 코드REANIMATOR: Reanimate Retrieval Test Collections with Extracted and Synthetic Resources
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.