Papers with Code 아카이브
Image Retrieval
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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/pierre-jacob/ICCV2019-Horde
공식 코드Metric Learning With HORDE: High-Order Regularizer for Deep Embeddings
https://github.com/pinakinathc/fscoco
공식 코드FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context
https://github.com/prbonn/descriptor-dr
공식 코드https://github.com/project-lighter/lighter
공식 코드https://github.com/psinger/kaggle-landmark-recognition-2020-1st-place
공식 코드Supporting large-scale image recognition with out-of-domain samples
https://github.com/pter61/context-i2w
공식 코드https://github.com/pter61/predicir
공식 코드https://github.com/pultarmi/HardNet_MultiDataset
공식 코드https://github.com/puneesh00/loop
공식 코드LoOp: Looking for Optimal Hard Negative Embeddings for Deep Metric Learning
https://github.com/pwenjay/gca-hng
공식 코드https://github.com/pxiangwu/forb
공식 코드FORB: A Flat Object Retrieval Benchmark for Universal Image Embedding
https://github.com/qinnzou/Zero-Shot-Hashing
공식 코드Transductive Zero-Shot Hashing for Multilabel Image Retrieval
https://github.com/qliu24/SAKE
공식 코드Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval
https://github.com/raivnlab/crepe
공식 코드CREPE: Can Vision-Language Foundation Models Reason Compositionally?
https://github.com/ramdrop/rcir
공식 코드https://github.com/ranarag/ZSCRGAN
공식 코드https://github.com/realize-lab/chair
공식 코드https://github.com/research2020-1/DSTDH
공식 코드Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
https://github.com/riedlerm/multimodal_rag_for_industry
공식 코드Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial Applications
https://github.com/roboflow-ai/roboflow-100-benchmark
공식 코드Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark
https://github.com/rogerferrod/rsicrc
공식 코드Towards a multimodal framework for remote sensing image change retrieval and captioning
https://github.com/rohit102497/mabnet
공식 코드MABNet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval
https://github.com/rongchengtu1/uhscm
공식 코드https://github.com/saehyung-lee/plugir
공식 코드Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach
https://github.com/safouaneelg/copulasimilarity
공식 코드CSIM: A Copula-based similarity index sensitive to local changes for Image quality assessment
https://github.com/salesforce/lavis
공식 코드https://github.com/samaonline/Orthogonal-Convolutional-Neural-Networks
공식 코드https://github.com/samia067/crisp
공식 코드CriSp: Leveraging Tread Depth Maps for Enhanced Crime-Scene Shoeprint Matching
https://github.com/saraao/amodal-dataset
공식 코드Amodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmark
https://github.com/saraghazanfari/lipsim
공식 코드https://github.com/satoru2001/cukdfir
공식 코드https://github.com/sayannath/identical-image-retrieval
공식 코드https://github.com/scientific-computing-nrcn/SimulAI
공식 코드Complete CVDL Methodology for Investigating Hydrodynamic Instabilities
https://github.com/sdc17/crossget
공식 코드CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers
https://github.com/sdc17/upop
공식 코드UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.