https://github.com/layumi/Image-Text-Embedding
공식 코드Dual-Path Convolutional Image-Text Embeddings with Instance Loss
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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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/layumi/Oxford-Paris-Attack
공식 코드Query Attack via Opposite-Direction Feature:Towards Robust Image Retrieval
https://github.com/layumi/Pedestrian_Alignment
공식 코드Pedestrian Alignment Network for Large-scale Person Re-identification
https://github.com/lbox-kr/nestle
공식 코드NESTLE: a No-Code Tool for Statistical Analysis of Legal Corpus
https://github.com/lcschv/toma
공식 코드https://github.com/lcy80366872/knowledgeable-r1
공식 코드Knowledgeable-r1: Policy Optimization for Knowledge Exploration in Retrieval-Augmented Generation
https://github.com/ldilab/smr
공식 코드From Token to Action: State Machine Reasoning to Mitigate Overthinking in Information Retrieval
https://github.com/ldzhangyx/bart-fusion
공식 코드Interpreting Song Lyrics with an Audio-Informed Pre-trained Language Model
https://github.com/lean-dojo/leandojo
공식 코드LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
https://github.com/lean-dojo/leandojochatgpt
공식 코드LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
https://github.com/lean-dojo/reprover
공식 코드LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
https://github.com/leaplabthu/cross-modal-adapter
공식 코드https://github.com/leeyn-43/clover
공식 코드Clover: Towards A Unified Video-Language Alignment and Fusion Model
https://github.com/leezekun/macrag
공식 코드MacRAG: Compress, Slice, and Scale-up for Multi-Scale Adaptive Context RAG
https://github.com/leililab/rare
공식 코드RARE: Retrieval-Aware Robustness Evaluation for Retrieval-Augmented Generation Systems
https://github.com/leiluk1/codesearcher
공식 코드https://github.com/leisemann/ca_mmde
공식 코드Introducing a Class-Aware Metric for Monocular Depth Estimation: An Automotive Perspective
https://github.com/lelan-li/SSAH
공식 코드Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
https://github.com/lemire/lbimproved
공식 코드Faster Retrieval with a Two-Pass Dynamic-Time-Warping Lower Bound
https://github.com/lemonjust/napari-vodex
공식 코드VoDEx: a Python library for time annotation and management of volumetric functional imaging data
https://github.com/lemonjust/vodex
공식 코드VoDEx: a Python library for time annotation and management of volumetric functional imaging data
https://github.com/lemuria-wchen/CFC
공식 코드https://github.com/lenkatetkova/knowledge-graphs-for-empirical-concept-retrieval
공식 코드https://github.com/leolee99/ld-agent
공식 코드Hello Again! LLM-powered Personalized Agent for Long-term Dialogue
https://github.com/leolee99/pau
공식 코드Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval
https://github.com/leom93/biologicalrandomwalks
공식 코드Biological Random Walks: multi-omics integration for disease gene prioritization
https://github.com/leon1207/video-rag-master
공식 코드Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension
https://github.com/leox1v/query_decoder
공식 코드Decoding a Neural Retriever's Latent Space for Query Suggestion
https://github.com/lerogo/aaai24_itr_cusa
공식 코드Cross-Modal and Uni-Modal Soft-Label Alignment for Image-Text Retrieval
https://github.com/levelfour/pumil
공식 코드Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags
https://github.com/levymsn/ChatIR
공식 코드https://github.com/levymsn/LaSCo
공식 코드Data Roaming and Quality Assessment for Composed Image Retrieval
https://github.com/lewisjiang/contour-context
공식 코드https://github.com/leymir/hyperbolic-image-embeddings
공식 코드https://github.com/lezhang7/Enhance-FineGrained
공식 코드https://github.com/lezhang7/moqagpt
공식 코드MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.