https://github.com/danielchyeh/this-is-my
공식 코드Meta-Personalizing Vision-Language Models to Find Named Instances in Video
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 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/danieljf24/cbvr
공식 코드Feature Re-Learning with Data Augmentation for Video Relevance Prediction
https://github.com/danieljf24/dual_encoding
공식 코드https://github.com/danieljf24/text2image
공식 코드Cross-Media Similarity Evaluation for Web Image Retrieval in the Wild
https://github.com/danieljf24/w2vv
공식 코드Predicting Visual Features from Text for Image and Video Caption Retrieval
https://github.com/daniilidis-group/emvn
공식 코드https://github.com/daniilidis-group/spherical-cnn
공식 코드Learning SO(3) Equivariant Representations with Spherical CNNs
https://github.com/daniilidis-group/swscnn
공식 코드https://github.com/dannigt/kit-iwslt2023-multilingual
공식 코드https://github.com/daod/inters
공식 코드INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning
https://github.com/daod/spring
공식 코드https://github.com/daqingchong/Q2ID-benchmark-dataset
공식 코드https://github.com/darkpromise98/iaa
공식 코드Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning
https://github.com/data-iitd/neuroseqret
공식 코드Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences
https://github.com/datascienceuibk/arabicaqa
공식 코드ArabicaQA: A Comprehensive Dataset for Arabic Question Answering
https://github.com/datascienceuibk/hintqa
공식 코드Exploring Hint Generation Approaches in Open-Domain Question Answering
https://github.com/datovar4/ai_literature_review_suite
공식 코드https://github.com/davidabrahamyan/stackrag
공식 코드StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation
https://github.com/davidetalon/fashionact
공식 코드Seeing the Abstract: Translating the Abstract Language for Vision Language Models
https://github.com/davidmrau/transformer-vs-bm25
공식 코드How Different are Pre-trained Transformers for Text Ranking?
https://github.com/davidoterof/ecir2025
공식 코드Towards Reliable Testing for Multiple Information Retrieval System Comparisons
https://github.com/davidzwz/awesome-deep-research
공식 코드From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
https://github.com/davion-liu/awesome-robustness-in-information-retrieval
공식 코드Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective
https://github.com/davion-liu/gr_ood
공식 코드On the Robustness of Generative Information Retrieval Models
https://github.com/dayoon-ko/GrowOVER
공식 코드GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge?
https://github.com/dayuyang1999/zeqr
공식 코드ZeQR: Zero-shot Query Reformulation for Conversational Search
https://github.com/dbaranchuk/ivf-hnsw
공식 코드Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors
https://github.com/dbmi-pitt/bioinf_teachingNLP
공식 코드Introducing Information Retrieval for Biomedical Informatics Students
https://github.com/dbstjswo505/SQuiDNet
공식 코드Selective Query-guided Debiasing for Video Corpus Moment Retrieval
https://github.com/dcp15/ual
공식 코드Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval
https://github.com/ddw2aigroup2cqupt/mgrl
공식 코드Multi-Granularity Representation Learning for Sketch-based Dynamic Face Image Retrieval
https://github.com/ddw2aigroup2cqupt/slfir
공식 코드https://github.com/dealicious-inc/dtop
공식 코드https://github.com/debrup-61/mathsensei
공식 코드MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning
https://github.com/declare-lab/exemplary-empathy
공식 코드Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication
https://github.com/declare-lab/trust-align
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.