https://github.com/qyr0403/reversed-in-time
공식 코드Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text Retrieval
Papers with Code 아카이브
Retrieval
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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/qzc438-research/ontology-llm
공식 코드https://github.com/qzc438/ontology-llm
공식 코드https://github.com/r-cui/ViGA
공식 코드Video Moment Retrieval from Text Queries via Single Frame Annotation
https://github.com/r9y9/wavenet_vocoder
공식 코드https://github.com/rahmanidashti/synthetictestcollections
공식 코드https://github.com/raian08/lw2g
공식 코드LW2G: Learning Whether to Grow for Prompt-based Continual Learning
https://github.com/rainym00d/llm4rs
공식 코드https://github.com/raivnlab/adanns
공식 코드https://github.com/raivnlab/crepe
공식 코드CREPE: Can Vision-Language Foundation Models Reason Compositionally?
https://github.com/raivnlab/mrl
공식 코드https://github.com/raja-kumar/knowledge-grounded-tods
공식 코드Task Oriented Conversational Modelling With Subjective Knowledge
https://github.com/rajpurkarlab/x-rem
공식 코드Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation
https://github.com/rakutentech/hq
공식 코드Efficient Cross-Modal Retrieval via Deep Binary Hashing and Quantization
https://github.com/raldir/aut-few
공식 코드Automated Few-shot Classification with Instruction-Finetuned Language Models
https://github.com/ram2c/ram2c
공식 코드https://github.com/ramakanth-pasunuru/video-dialogue
공식 코드https://github.com/ramdrop/rcir
공식 코드https://github.com/ranarag/UnsupClean
공식 코드https://github.com/ranarag/ZSCRGAN
공식 코드https://github.com/ranking-vmr/spr
공식 코드https://github.com/ranking-vmr/tvr-ranking
공식 코드TVR-Ranking: A Dataset for Ranked Video Moment Retrieval with Imprecise Queries
https://github.com/raosudha89/clarification_question_generation_pytorch
공식 코드Answer-based Adversarial Training for Generating Clarification Questions
https://github.com/raphaelmerx/mambai
공식 코드https://github.com/rapidsai/cuml
공식 코드https://github.com/rapidsai/raft
공식 코드https://github.com/raruidol/budget-am
공식 코드https://github.com/ratschlab/genomic-gnn
공식 코드Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs
https://github.com/razvanip13/towards_lighter_and_robust_evaluation
공식 코드Towards Lighter and Robust Evaluation for Retrieval Augmented Generation
https://github.com/razzu/cmc-cmkm
공식 코드Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition
https://github.com/rcgai/simplyretrieve
공식 코드SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool
https://github.com/rea1991/SHREC2021
공식 코드https://github.com/real-doris-mae/doris-mae-dataset
공식 코드DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries
https://github.com/realize-lab/chair
공식 코드https://github.com/realtimeqa/realtimeqa_public
공식 코드https://github.com/recombee/compressae
공식 코드The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.