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/khazarkhorrami/VGS_alignment
Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models
https://github.com/khoadoan106/single_loss_quantization
One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching
https://github.com/kingcong/delf
Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/kingcong/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/kmkwon94/ainize-laser
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
https://github.com/kornia/kornia
Working hard to know your neighbor's margins: Local descriptor learning loss
https://github.com/kornia/kornia/blob/master/kornia/feature/affine_shape.py
Repeatability Is Not Enough: Learning Affine Regions via Discriminability
https://github.com/kornia/kornia/blob/master/kornia/feature/sosnet.py
SOSNet: Second Order Similarity Regularization for Local Descriptor Learning
https://github.com/koujm/convert-tf
ConveRT: Efficient and Accurate Conversational Representations from Transformers
https://github.com/kracr/document-level-relation-extraction
https://github.com/krutikabapat/Inverse_Cooking_recipe_Generation_from_food_images
https://github.com/krypticmouse/matryoshka-representation-learning
https://github.com/ku-nlp/bert-based-faqir
FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance
https://github.com/kunhe/TALR
https://github.com/kwanhui/finrelextract
https://github.com/kyamagu/js-segment-annotator
https://github.com/kyegomez/CM3Leon
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
https://github.com/kyegomez/PALI3
https://github.com/kylrth/procedure-generation
https://github.com/kyriemao/TagGNN-SIGIR
Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
https://github.com/kzhou23/shape_pose_disent
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/laion-ai/clip_benchmark
https://github.com/lannguyen0910/deep-efficient-person-reid
On the Unreasonable Effectiveness of Centroids in Image Retrieval
https://github.com/lannguyen0910/deep-efficient-reid
On the Unreasonable Effectiveness of Centroids in Image Retrieval
https://github.com/lastmile-ai/aiconfig/tree/main/cookbooks/Chain-of-Verification
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
https://github.com/laura-wang/video_repres_mas
Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics
https://github.com/lauradhatt/Interesting-Reads
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
https://github.com/laurinwagner/grouploss_plus
https://github.com/lavender105/rsgpt
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
https://github.com/layer6ai-labs/GSS
https://github.com/layer6ai-labs/direct-cms
https://github.com/layumi/Person-reID-verification
A Discriminatively Learned CNN Embedding for Person Re-identification
https://github.com/layumi/Person_reID_baseline_pytorch
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/layumi/University1652-Baseline
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.