Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/filipradenovic/cnnimageretrieval
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
https://github.com/fireflyHunter/OpenNMT-Livebot
LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts
https://github.com/fla-org/flash-linear-attention
https://github.com/florianlanger/leveraging_geometry_for_shape_estimation
Leveraging Geometry for Shape Estimation from a Single RGB Image
https://github.com/flyingsheepbin/pet-biometrics
Combination of Multiple Global Descriptors for Image Retrieval
https://github.com/flytocc/PaddleClas
https://github.com/fmaglia/BoI
Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition
https://github.com/fmaglia/LSH_kNN_graph
An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval
https://github.com/fmaglia/keras_rmac_plus
An accurate retrieval through R-MAC+ descriptors for landmark recognition
https://github.com/fmenat/DiscreteVAE
https://github.com/fongyk/graph-attention
End-to-end Learning of Deep Visual Representations for Image Retrieval
https://github.com/fongyk/graph-simple
End-to-end Learning of Deep Visual Representations for Image Retrieval
https://github.com/francois-rozet/mmps-benchmark
Diffusion Posterior Sampling for General Noisy Inverse Problems
https://github.com/freshllms/freshqa
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
https://github.com/fuqianya/ViLBERT-Paddle
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/fwilliams/fml
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
https://github.com/fwilliams/point-cloud-utils
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
https://github.com/fwilliams/scalable-pytorch-sinkhorn
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
https://github.com/fxia22/kdnet.pytorch
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
https://github.com/gabeur/mmt
https://github.com/gangiswag/llm-reranker
https://github.com/garygsw/Nav-NNDial
https://github.com/garythung/torch-lrcn
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/gchb2012/VQA
Cross-media Multi-level Alignment with Relation Attention Network
https://github.com/geekysethi/triplet-GAN
https://github.com/gengchenmai/se-kge
https://github.com/gentopia-ai/gentopia
ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models
https://github.com/gerasmark/Reproducing-Unseen-Modality-Interaction
https://github.com/ghost1412/CRAFT
https://github.com/gicheonkang/gst-visdial
https://github.com/ginihumer/amumo
https://github.com/giovanniguidi/logo-detection
https://github.com/giuseppefutia/semi
https://github.com/gjacopo/poppysite
PlaNet - Photo Geolocation with Convolutional Neural Networks
https://github.com/glb400/Toy-RecLM
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.