https://github.com/souvikshanku/digit-captioning
Deep Visual-Semantic Alignments for Generating Image Descriptions
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/speckean/upar_challenge
UPAR: Unified Pedestrian Attribute Recognition and Person Retrieval
https://github.com/ssharoff/cognates
Improving zero-shot learning by mitigating the hubness problem
https://github.com/sssohrab/sparsifying_groups_imAmbiguation
Learning to compress and search visual data in large-scale systems
https://github.com/ssundaram21/dreamsim
When Does Perceptual Alignment Benefit Vision Representations?
https://github.com/stanfordnlp/cocoa
https://github.com/stanfordnlp/dspy
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
https://github.com/stanfordvl/atp-video-language
https://github.com/stdio2016/pfann
Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning
https://github.com/stefan-ainetter/HOC-Search
HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans
https://github.com/stefan-ainetter/SCANnotateDataset
HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans
https://github.com/stephen2526/structure-informed_plm
https://github.com/stnoah1/mcb
https://github.com/stormieGal/mediLearn
https://github.com/stschubert/vpr_tutorial
EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition
https://github.com/sudhir5595/Mesh_Neural_Network
https://github.com/sujaygarlanka/tennis_stroke_recognition
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/sung-yeon-kim/GENIUS-CVPR25
GENIUS: A Generative Framework for Universal Multimodal Search
https://github.com/sunhwan-lee/recommender_system
Deep Learning based Recommender System: A Survey and New Perspectives
https://github.com/sunn-e/DeepFashion-retrieval-2019
Fine-grained Apparel Classification and Retrieval without rich annotations
https://github.com/sunniesuhyoung/iMet2020cleaned
https://github.com/sunnweiwei/genret
https://github.com/superli3/CYRMPR
code2seq: Generating Sequences from Structured Representations of Code
https://github.com/superli3/codenavi
code2seq: Generating Sequences from Structured Representations of Code
https://github.com/surajiyer/Recommender-Systems
https://github.com/suruoxi/DistanceWeightedSampling
https://github.com/suryanshkumar/online-joint-depthfusion-and-semantic
ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
https://github.com/susurrant/flow-imputation
https://github.com/susurrant/spatial-interaction-modeling
https://github.com/syfafterzy/PCB_RPP
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/syfafterzy/pcb_rpp_for_reid
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/szq0214/CMC_with_Image_Mixture
Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
https://github.com/szubing/uniood
https://github.com/taesunwhang/BERT-ResSel
An Effective Domain Adaptive Post-Training Method for BERT in Response Selection
https://github.com/taesunwhang/MVAN-VisDial
https://github.com/taki0112/AdamP-Tensorflow
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.