https://github.com/swyoon/aaai2021-scene-graph-img-retr
공식 코드Image-to-Image Retrieval by Learning Similarity between Scene Graphs
Papers with Code 아카이브
Image Retrieval
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- 평가표
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Image Retrieval is a fundamental and long-standing computer vision task that involves finding images similar to a given query from a large database. It is often considered a form of fine-grained, instance-level classification. The task is integral to image recognition alongside classification and cross-modal retrieval. By leveraging visual similarity and other criteria, image retrieval enables users to efficiently discover relevant images, making it a crucial tool in applications such as search and recommendation.
Extending CLIP for Category-to-image Retrieval in E-commerce
<span style="color:grey; opacity: 0.6">( Image credit: DELF )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/syqlyx/StructVPR
공식 코드https://github.com/szeighami/nudge
공식 코드NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval
https://github.com/taco-group/re-align
공식 코드Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization
https://github.com/tavisshore/BEV-CV
공식 코드BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation
https://github.com/tavisshore/PEnG
공식 코드https://github.com/tavisshore/SpaGBOL
공식 코드https://github.com/tawald/semantic_rsm
공식 코드Decoupling Semantic Similarity from Spatial Alignment for Neural Networks
https://github.com/tensorflow/models
공식 코드Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/tfburns/cdam
공식 코드Semantically-correlated memories in a dense associative model
https://github.com/thezjd/aspectmmkg
공식 코드AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities
https://github.com/thomas-yin/semeval-2023-task1
공식 코드https://github.com/thongnt99/lsr-multimodal
공식 코드Multimodal Learned Sparse Retrieval with Probabilistic Expansion Control
https://github.com/thudm/cogvlm
공식 코드https://github.com/thudzj/NEigenmaps
공식 코드Neural Eigenfunctions Are Structured Representation Learners
https://github.com/thuml/DPH
공식 코드https://github.com/timqqt/fair_text_based_image_retrieval
공식 코드https://github.com/tingwei-zhang/adv_hub
공식 코드https://github.com/tinkoff-ai/probabilistic-embeddings
공식 코드https://github.com/tonyngjichun/SOLAR
공식 코드https://github.com/torrvision/straighttoshapes
공식 코드Straight to Shapes++: Real-time Instance Segmentation Made More Accurate
https://github.com/trent-tangtao/autoview
공식 코드Learning Self-Regularized Adversarial Views for Self-Supervised Vision Transformers
https://github.com/tsattler/understanding_apr
공식 코드Understanding the Limitations of CNN-based Absolute Camera Pose Regression
https://github.com/tsingqguo/jadena
공식 코드Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection
https://github.com/tudelft-iv/crossviewmetriclocalization
공식 코드Visual Cross-View Metric Localization with Dense Uncertainty Estimates
https://github.com/tudelft/risk-sensitive-rl
공식 코드https://github.com/uark-aicv/aisformer
공식 코드https://github.com/uark-aicv/shapeformer
공식 코드ShapeFormer: Shape Prior Visible-to-Amodal Transformer-based Amodal Instance Segmentation
https://github.com/uav-avl/benchmark
공식 코드https://github.com/ushasi/CMIR-NET-A-deep-learning-based-model-for-cross-modal-retrieval-in-remote-sensing
공식 코드CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing
https://github.com/uvavision/DrillDown
공식 코드Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries
https://github.com/uvavision/rerankingtransformer
공식 코드https://github.com/vaishwarya96/hierarchy-image-retrieval
공식 코드Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval
https://github.com/val-iisc/sketch-parse
공식 코드https://github.com/valley-vl/fashionlogo
공식 코드FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings
https://github.com/varunakk/imaginator
공식 코드IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.