Content-Based Image Retrieval
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Dual-Path Convolutional Image-Text Embedding with Instance Loss
In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space.
CONTENT-BASED IMAGE RETRIEVAL CROSS-MODAL RETRIEVAL PERSON RETRIEVAL TEXTURE IMAGE RETRIEVAL
Combination of Multiple Global Descriptors for Image Retrieval
arXiv 2019
• naver/cgd
• 
In the benchmark evaluation, the proposed framework achieves the state-of-the-art performance on the CARS196, CUB200-2011, In-shop Clothes and Stanford Online Products on image retrieval tasks by a large margin compared to competing approaches.
Content-based image retrieval tutorial
This paper functions as a tutorial for individuals interested to enter the field of information retrieval but wouldn't know where to begin from.
Classification is a Strong Baseline for Deep Metric Learning
Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images.
CONTENT-BASED IMAGE RETRIEVAL FACE VERIFICATION METRIC LEARNING
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image.
Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images
The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to a coarse distribution of sensors or sensor failures.
Who's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image Retrieval
• liuzrcc/PIRE
• 
An adversarial query is an image that has been modified to disrupt content-based image retrieval (CBIR) while appearing nearly untouched to the human eye.
Automatic Query Image Disambiguation for Content-Based Image Retrieval
Query images presented to content-based image retrieval systems often have various different interpretations, making it difficult to identify the search objective pursued by the user.
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags
Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances) and only bag labels are available.
CONTENT-BASED IMAGE RETRIEVAL MULTIPLE INSTANCE LEARNING TEXT CATEGORIZATION
Information-Theoretic Active Learning for Content-Based Image Retrieval
We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval.