Reducing label effort: Self-supervised meets active learning
JZ Bengar, J van de Weijer… - Proceedings of the …, 2021 - openaccess.thecvf.com
Active learning is a paradigm aimed at reducing the annotation effort by training the model
on actively selected informative and/or representative samples. Another paradigm to reduce …
on actively selected informative and/or representative samples. Another paradigm to reduce …
Deep reinforcement active learning for human-in-the-loop person re-identification
Most existing person re-identification (Re-ID) approaches achieve superior results based on
the assumption that a large amount of pre-labelled data is usually available and can be put …
the assumption that a large amount of pre-labelled data is usually available and can be put …
Content-based image retrieval and the semantic gap in the deep learning era
Content-based image retrieval has seen astonishing progress over the past decade,
especially for the task of retrieving images of the same object that is depicted in the query …
especially for the task of retrieving images of the same object that is depicted in the query …
Towards Efficient Emotion Self-report Collection Using Human-AI Collaboration: A Case Study on Smartphone Keyboard Interaction
Emotion-aware services are increasingly used in different applications such as gaming,
mental health tracking, video conferencing, and online tutoring. The core of such services is …
mental health tracking, video conferencing, and online tutoring. The core of such services is …
iSSL-AL: a deep active learning framework based on self-supervised learning for image classification
Deep neural networks have demonstrated exceptional performance across numerous
applications. However, DNNs require large amounts of labeled data to avoid overfitting …
applications. However, DNNs require large amounts of labeled data to avoid overfitting …
Advancing Image Retrieval with Few-Shot Learning and Relevance Feedback
With such a massive growth in the number of images stored, efficient search in a database
has become a crucial endeavor managed by image retrieval systems. Image Retrieval with …
has become a crucial endeavor managed by image retrieval systems. Image Retrieval with …
A Review and a Perspective of Deep Active Learning for Remote Sensing Image Analysis: Enhanced adaptation to user conjecture
In recent years, the application of deep learning (DL) has revolutionized remote sensing
(RS) image analysis, allowing for the extraction of high-level features, and addressing …
(RS) image analysis, allowing for the extraction of high-level features, and addressing …
Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval
Active Learning (AL) is a user-interactive approach aimed at reducing annotation costs by
selecting the most crucial examples to label. Although AL has been extensively studied for …
selecting the most crucial examples to label. Although AL has been extensively studied for …
Query by Example in Remote Sensing Image Archive Using Enhanced Deep Support Vector Data Description
O Ghozatlou, MH Conde… - IEEE Journal of Selected …, 2022 - ieeexplore.ieee.org
This article studies remote sensing image retrieval using kernel-based support vector data
description (SVDD). We exploit deep SVDD, which is a well-known method for one-class …
description (SVDD). We exploit deep SVDD, which is a well-known method for one-class …
Content-based image retrieval and the semantic gap in the deep learning era
Content-based image retrieval has seen astonishing progress over the past decade,
especially for the task of retrieving images of the same object that is depicted in the query …
especially for the task of retrieving images of the same object that is depicted in the query …