https://github.com/jacobdanovitch/jdnlp
공식 코드Linking Social Media Posts to News with Siamese Transformers
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/jacobluber/pathologysearchcomparison
공식 코드https://github.com/jacsonrbinf/bulner
공식 코드BULNER: BUg Localization with word embeddings and NEtwork Regularization
https://github.com/jadecurl/ffrr
공식 코드https://github.com/jahawn-wen/camel-reid
공식 코드CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person Retrieval
https://github.com/jakekrogh/3d-clip-for-brain-mri
공식 코드https://github.com/jakhac/csmae
공식 코드Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing
https://github.com/jamesding000/specgr
공식 코드Inductive Generative Recommendation via Retrieval-based Speculation
https://github.com/janesjanes/sketchy
공식 코드https://github.com/janinethoma/learning1M
공식 코드Learning Condition Invariant Features for Retrieval-Based Localization from 1M Images
https://github.com/jarobyte91/quotes
공식 코드https://github.com/jasaxion/irsc_benchmark
공식 코드https://github.com/jasoncodemaker/ctvr
공식 코드StableFusion: Continual Video Retrieval via Frame Adaptation
https://github.com/jasonppy/fast-vgs-family
공식 코드https://github.com/jasonustc/caffe-multigpu
공식 코드Transform-Invariant Convolutional Neural Networks for Image Classification and Search
https://github.com/jasonwu0731/GettingToKnowYou
공식 코드Getting To Know You: User Attribute Extraction from Dialogues
https://github.com/jasonyux/localrqa
공식 코드https://github.com/javyduck/knowhalu
공식 코드KnowHalu: Hallucination Detection via Multi-Form Knowledge Based Factual Checking
https://github.com/jaychempan/pir-clip
공식 코드PIR: Remote Sensing Image-Text Retrieval with Prior Instruction Representation Learning
https://github.com/jayleicn/ClipBERT
공식 코드Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling
https://github.com/jayleicn/TVCaption
공식 코드TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval
https://github.com/jayleicn/TVRetrieval
공식 코드TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval
https://github.com/jayleicn/mTVRetrieval
공식 코드https://github.com/jayleicn/moment_detr
공식 코드QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries
https://github.com/jayleicn/singularity
공식 코드https://github.com/jcjohnson/densecap
공식 코드DenseCap: Fully Convolutional Localization Networks for Dense Captioning
https://github.com/jcyk/copyisallyouneed
공식 코드Neural Machine Translation with Monolingual Translation Memory
https://github.com/jcyk/skeleton-to-response
공식 코드Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory
https://github.com/jd-aig/JAVE
공식 코드Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product
https://github.com/jdcomsearch/jdsearch-22
공식 코드Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search
https://github.com/jdcomsearch/poeem
공식 코드Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index
https://github.com/jdhao/deep_firearm
공식 코드DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval
https://github.com/jeffhj/raven
공식 코드RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models
https://github.com/jeffreyjohnens/style_rank
공식 코드Quantifying Musical Style: Ranking Symbolic Music based on Similarity to a Style
https://github.com/jeffwillette/cascading_kv_cache
공식 코드Training-Free Exponential Context Extension via Cascading KV Cache
https://github.com/jejjohnson/uncertain_gps
공식 코드Accounting for Input Noise in Gaussian Process Parameter Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.