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Content-Based Image Retrieval

1개 벤치마크 · 논문 203편 · 이 태스크의 논문 보기 →

Benchmarks

Most implemented

Content-based image retrieval tutorial

2016-08-12 · 구현 2개

Papers

Training-Free Pseudo-Fusion for Composed Image Retrieval with Diffusion Models and Multimodal Large Language Models

2026-08-24 · Fan Xu, Luis A. Leiva arxiv

Composed Image Retrieval (CIR) is an emerging paradigm in content-based image retrieval that enables users to formulate compositional queries by combining a reference image with an auxiliary modality, usually text-based.…

Content-Based Image Retrieval

Geometric Analysis of Self-Supervised Vision Representations for Semantic Image Retrieval

2026-04-27 · Esteban Rodríguez-Betancourt, Edgar Casasola-Murillo arxiv

Content-based image retrieval (CBIR) systems enable users to search images based on visual content instead of relying on metadata. The text domain has benefited from vector search of representations created with unsuperv…

Content-Based Image RetrievalSelf-Supervised LearningSemantic Retrieval

Evaluating the Impact of Data Anonymization on Image Retrieval

2026-02-23 · Marvin Chen, Manuel Eberhardinger, Johannes Maucher arxiv

With the growing importance of privacy regulations such as the General Data Protection Regulation, anonymizing visual data is becoming increasingly relevant across institutions. However, anonymization can negatively affe…

Content-Based Image Retrieval

Toward a Universal Color Naming System: A Clustering-Based Approach using Multisource Data

2026-01-30 · Aruzhan Sabitkyzy, Maksat Shagyrov, Pakizar Shamoi arxiv

Is it coral, salmon, or peach? What seems like a simple color can have many names, and without a standard, these variations create confusion across design, technology, and communication. Color naming is a fundamental tas…

Content-Based Image Retrieval

Acquisition of interpretable domain information during brain MR image harmonization for content-based image retrieval

2025-10-16 · Keima Abe, Hayato Muraki, Shuhei Tomoshige, Kenichi Oishi 외 arxiv

Medical images like MR scans often show domain shifts across imaging sites due to scanner and protocol differences, which degrade machine learning performance in tasks such as disease classification. Domain harmonization…

Content-Based Image RetrievalRepresentation LearningImage ReconstructionImage Harmonization

CSMoE: An Efficient Remote Sensing Foundation Model with Soft Mixture-of-Experts

2025-09-17 · Leonard Hackel, Tom Burgert, Begüm Demir arxiv

Self-supervised learning (SSL) through masked autoencoders (MAEs) has recently attracted great attention for remote sensing (RS) foundation model (FM) development, enabling improved representation learning across diverse…

Content-Based Image RetrievalSelf-Supervised LearningComputational EfficiencyRepresentation Learning

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