Papers with Code 아카이브
Image Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 10
- 평가표
- 56
- 보존된 평가 기록
- 372
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/compvis/metric-learning-divide-and-conquer-improved
공식 코드https://github.com/conghuihu/ucdir
공식 코드Feature Representation Learning for Unsupervised Cross-domain Image Retrieval
https://github.com/cruvadom/Logit_Separation
공식 코드Fast Single-Class Classification and the Principle of Logit Separation
https://github.com/cszhanglmu/obd-finder
공식 코드OBD-Finder: Explainable Coarse-to-Fine Text-Centric Oracle Bone Duplicates Discovery
https://github.com/cugbrs/MLRSNet
공식 코드https://github.com/curisejia/freestyleret
공식 코드FreestyleRet: Retrieving Images from Style-Diversified Queries
https://github.com/cuthbertcai/ask-confirm
공식 코드Ask&Confirm: Active Detail Enriching for Cross-Modal Retrieval with Partial Query
https://github.com/cv-xueba/image-layout-learning
공식 코드Self-supervised Photographic Image Layout Representation Learning
https://github.com/cvdfoundation/google-landmark
공식 코드Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval
https://github.com/cvjena/ITAL
공식 코드Information-Theoretic Active Learning for Content-Based Image Retrieval
https://github.com/cvjena/aid
공식 코드Automatic Query Image Disambiguation for Content-Based Image Retrieval
https://github.com/cvjena/eu-flood-dataset
공식 코드Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images
https://github.com/cvjena/semantic-embeddings
공식 코드Hierarchy-based Image Embeddings for Semantic Image Retrieval
https://github.com/cyclomon/oneshotclip
공식 코드https://github.com/d-zabihzadeh/Adaptive-Adv-DML
공식 코드https://github.com/dandelin/vilt
공식 코드ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
https://github.com/danieljf24/text2image
공식 코드Cross-Media Similarity Evaluation for Web Image Retrieval in the Wild
https://github.com/darkpromise98/iaa
공식 코드Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning
https://github.com/davidetalon/fashionact
공식 코드Seeing the Abstract: Translating the Abstract Language for Vision Language Models
https://github.com/dcp15/ual
공식 코드Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval
https://github.com/ddw2aigroup2cqupt/mgrl
공식 코드Multi-Granularity Representation Learning for Sketch-based Dynamic Face Image Retrieval
https://github.com/ddw2aigroup2cqupt/slfir
공식 코드https://github.com/dealicious-inc/dtop
공식 코드https://github.com/deep-spin/HFYN
공식 코드Hopfield-Fenchel-Young Networks: A Unified Framework for Associative Memory Retrieval
https://github.com/deepaknlp/dls
공식 코드Medical Image Retrieval via Nearest Neighbor Search on Pre-trained Image Features
https://github.com/deepglint/unicom
공식 코드Unicom: Universal and Compact Representation Learning for Image Retrieval
https://github.com/deepmind/multimodal_transformers
공식 코드Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers
https://github.com/deepmind/svo_probes
공식 코드https://github.com/deezer/sigir2019-2stagesampling
공식 코드Improving Collaborative Metric Learning with Efficient Negative Sampling
https://github.com/dell-research-harvard/effocr
공식 코드https://github.com/dmmm1997/drl
공식 코드https://github.com/dmmm1997/fsra
공식 코드A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization
https://github.com/donglao/srt
공식 코드Sub-token ViT Embedding via Stochastic Resonance Transformers
https://github.com/doodleui/wnd
공식 코드https://github.com/drsy/motis
공식 코드Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.