https://github.com/computer-vision-in-the-wild/cvinw_readings
공식 코드Vision-Language Pre-training: Basics, Recent Advances, and Future Trends
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/compvis/latent-diffusion
공식 코드https://github.com/compvis/metric-learning-divide-and-conquer-improved
공식 코드https://github.com/conghuihu/ucdir
공식 코드Feature Representation Learning for Unsupervised Cross-domain Image Retrieval
https://github.com/congressRA/sample-agent
공식 코드Agent-Enhanced Large Language Models for Researching Political Institutions
https://github.com/contextualai/gritlm
공식 코드https://github.com/control-xl/medical-vision-langauge-transformer
공식 코드https://github.com/cooelf/DeepUtteranceAggregation
공식 코드Modeling Multi-turn Conversation with Deep Utterance Aggregation
https://github.com/cooliotonyio/dime
공식 코드DIME: An Online Tool for the Visual Comparison of Cross-Modal Retrieval Models
https://github.com/copenlu/context-utilisation-for-rag
공식 코드A Reality Check on Context Utilisation for Retrieval-Augmented Generation
https://github.com/corxyz/icl-cmr
공식 코드Linking In-context Learning in Transformers to Human Episodic Memory
https://github.com/cosimorulli/emvb
공식 코드https://github.com/cpjku/performance_embeddings_fire23
공식 코드https://github.com/crealabs/enhanced-bge-m3-with-clp-and-moe
공식 코드https://github.com/cristal-3dsam/humancomparisonvarifolds
공식 코드3D Shape Sequence of Human Comparison and Classification using Current and Varifolds
https://github.com/cristian-rusu-research/shift-invariance
공식 코드https://github.com/crossing-minds/shopping-queries-image-dataset
공식 코드https://github.com/csarron/btr
공식 코드BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models
https://github.com/cscribano/AYCE_2021
공식 코드All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers
https://github.com/csebuetnlp/crosssum
공식 코드CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs
https://github.com/csehong/VM-NET
공식 코드Content-Based Video-Music Retrieval Using Soft Intra-Modal Structure Constraint
https://github.com/csguoh/refir
공식 코드ReFIR: Grounding Large Restoration Models with Retrieval Augmentation
https://github.com/cshaitao/jtr
공식 코드Constructing Tree-based Index for Efficient and Effective Dense Retrieval
https://github.com/cshaitao/lexrag
공식 코드LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation
https://github.com/cshaitao/sailer
공식 코드SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval
https://github.com/cshaitao/thuir-coliee2023
공식 코드https://github.com/csiro-robotics/LoGG3D-Net
공식 코드LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition
https://github.com/csiro-robotics/p-gat
공식 코드Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition
https://github.com/csiro-robotics/spectralgv
공식 코드Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization
https://github.com/csong27/collision-bert
공식 코드https://github.com/cszhanglmu/obd-finder
공식 코드OBD-Finder: Explainable Coarse-to-Fine Text-Centric Oracle Bone Duplicates Discovery
https://github.com/cszyzhang/riconv2
공식 코드RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
https://github.com/ct123098/factoid-wiki
공식 코드Dense X Retrieval: What Retrieval Granularity Should We Use?
https://github.com/ctsutake/dsr
공식 코드Compressing Sign Information in DCT-based Image Coding via Deep Sign Retrieval
https://github.com/cugbrs/MLRSNet
공식 코드https://github.com/curai/curai-research
공식 코드OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.