https://github.com/apacha/OMR-Datasets
Optical Music Recognition with Convolutional Sequence-to-Sequence Models
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/applied-machine-learning-lab/awesome-multimodal-recommender-systems
Implicit semantic-based personalized micro-videos recommendation
https://github.com/arcadelab/DeepDRR
Localizing dexterous surgical tools in X-ray for image-based navigation
https://github.com/arijitray1993/COLA
https://github.com/armandvilalta/Full-network-multimodal-embeddings
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/arpytanshu/HUSE-PyTorch
https://github.com/arthur-heng/nlgraph
Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models
https://github.com/arvind385801/paraphrasegen
https://github.com/asfathermou/human-computer-interaction
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
https://github.com/ashikkapadiya/Video-Motion-Retargeting
Learning Character-Agnostic Motion for Motion Retargeting in 2D
https://github.com/ashishdasari148/Recommender-Systems-using-Collaborative-Filtering
Neural Collaborative Filtering vs. Matrix Factorization Revisited
https://github.com/assafelovic/gpt-researcher
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/astro-informatics/rcGAN
A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems
https://github.com/autoliuweijie/bert-whitening-pytorch
Whitening Sentence Representations for Better Semantics and Faster Retrieval
https://github.com/avalonstrel/SketchBERT
https://github.com/avidale/arxivarius
Training Neural Response Selection for Task-Oriented Dialogue Systems
https://github.com/avsolatorio/gistembed
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
https://github.com/awni00/abstract_transformer
Disentangling and Integrating Relational and Sensory Information in Transformer Architectures
https://github.com/axrwl/bidirectional-cross-attention
Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers
https://github.com/aysim/comingdowntoearth
Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization
https://github.com/baai-dcai/segvol
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
https://github.com/baaivision/GeoDream
https://github.com/babylonhealth/fastText_multilingual
Improving zero-shot learning by mitigating the hubness problem
https://github.com/baichuan/disambiguation_embedding
Name Disambiguation in Anonymized Graphs using Network Embedding
https://github.com/baldandbrave/RecSysCOEN6313
https://github.com/baldassarreFe/deep-koalarization
CNN Features off-the-shelf: an Astounding Baseline for Recognition
https://github.com/bangoc123/drop-rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/bangoc123/retrieval-backend-with-rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/basf/chemteb
https://github.com/batra-mlp-lab/visdial
https://github.com/batra-mlp-lab/visdial-challenge-starter-pytorch
https://github.com/bebr2/lekube
STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals
https://github.com/behavioral-data/cognitive-reframing
Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction
https://github.com/bekou/evidence_aware_nlp4if
https://github.com/berlincho/RGCN-pytorch
https://github.com/bethgelab/frequency_determines_performance
SuS-X: Training-Free Name-Only Transfer of Vision-Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.