https://github.com/KuofengGao/CIBA
공식 코드Backdoor Attack on Hash-based Image Retrieval via Clean-label Data Poisoning
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/LARC-CMU-SMU/ACME
공식 코드Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images
https://github.com/LHRLAB/HyperGraphRAG
공식 코드HyperGraphRAG: Retrieval-Augmented Generation with Hypergraph-Structured Knowledge Representation
https://github.com/LIN-SHANG/InstructERC
공식 코드https://github.com/LLaVA-VL/LLaVA-Plus-Codebase
공식 코드LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
https://github.com/Leolty/repobench
공식 코드RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems
https://github.com/LequnWang/Improve-Screening-via-Calibrated-Subset-Selection
공식 코드Improving Screening Processes via Calibrated Subset Selection
https://github.com/LexPredict/openedgar
공식 코드https://github.com/Li-Z-Q/DeepSolution
공식 코드https://github.com/LiqunChen0606/Graph-Optimal-Transport
공식 코드https://github.com/LittleYUYU/CoaCor
공식 코드CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning
https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset
공식 코드StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow
https://github.com/Liu-Yating/CSKT
공식 코드CLIP-based Synergistic Knowledge Transfer for Text-based Person Retrieval
https://github.com/LiuChang97/CLMN
공식 코드Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce
https://github.com/Liumouliu/deepIBL
공식 코드Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization
https://github.com/Lizonghang/PerSF-SemCom
공식 코드https://github.com/LooKing9218/RetiZero
공식 코드https://github.com/Lsyhprum/WEAVESS
공식 코드https://github.com/Lu-Feng/SelaVPR
공식 코드SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition
https://github.com/LuciusLan/Visual-RAG
공식 코드https://github.com/LuoXiaoHeics/PerSphere
공식 코드PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization
https://github.com/Luoyadan/MAH-Pytorch
공식 코드Collaborative Learning for Extremely Low Bit Asymmetric Hashing
https://github.com/M-Elfeki/ThirdToFirst
공식 코드From Third Person to First Person: Dataset and Baselines for Synthesis and Retrieval
https://github.com/MAGICS-LAB/Chain-of-Actions
공식 코드Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models
https://github.com/MKLab-ITI/visil
공식 코드ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning
https://github.com/MKegler/SpeechVGG
공식 코드https://github.com/MLEnthusiast/MHCLN
공식 코드Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images
https://github.com/MLforHealth/S2SD
공식 코드S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
https://github.com/MMIV-ML/DLMI2018
공식 코드An overview of deep learning in medical imaging focusing on MRI
https://github.com/MaJinWakeUp/HeWR
공식 코드Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval
https://github.com/Maddy12/MultiModalVideoRobustness/tree/master/code
공식 코드Robustness Analysis of Video-Language Models Against Visual and Language Perturbations
https://github.com/MalongTech/research-ms-loss
공식 코드Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning
https://github.com/MalongTech/research-xbm
공식 코드https://github.com/Mandeep-Rathee/quam
공식 코드https://github.com/Mao-KU/lightweight-crosslingual-sent2vec
공식 코드https://github.com/MarekKowalski/HoloFace
공식 코드HoloFace: Augmenting Human-to-Human Interactions on HoloLens
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.