ArticleACM computing surveys2020
Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise.
Article in ACM computing surveys, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Development and validation of a deep learning model for radiographic classification of pediatric femoral neck fractures.Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology · 2026Article
- Photonic biosensors based on nanoparticle superstructures: from data analysis to artificial intelligence (AI) detection.Fundamental research · 2026Review
- MvAl-MFP: A Multi-Label Classification Method on the Functions of Peptides with Multi-View Active Learning.Current issues in molecular biology · 2025Article
- OMAL: A Multi-Label Active Learning Approach from Data Streams.Entropy (Basel, Switzerland) · 2025Article
- Enhanced uncertainty sampling with category information for improved active learning.PloS one · 2025Article
- Image dataset for benchmarking automated fish detection and classification algorithms.Scientific data · 2023Article
- An adaptive decision-making system supported on user preference predictions for human-robot interactive communication.User modeling and user-adapted interaction · 2023Article
- Multi-classification of national fitness test grades based on statistical analysis and machine learning.PloS one · 2023Article
- From Knowledge Transmission to Knowledge Construction: A Step towards Human-Like Active Learning.Entropy (Basel, Switzerland) · 2020Article
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Authors and funding
8 authors.
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Abstract
Image classification is a key task in image understanding, and multi-label image classification has become a popular topic in recent years. However, the success of multi-label image classification is closely related to the way of constructing a training set. As active learning aims to construct an effective training set through iteratively selecting the most informative examples to query labels from annotators, it was introduced into multi-label image classification. Accordingly, multi-label active learning is becoming an important research direction. In this work, we first review existing multi-label active learning algorithms for image classification. These algorithms can be categorized into two top groups from two aspects respectively: sampling and annotation. The most important component of multi-label active learning is to design an effective sampling strategy that actively selects the examples with the highest informativeness from an unlabeled data pool, according to various information measures. Thus, different informativeness measures are emphasized in this survey. Furthermore, this work also makes a deep investigation on existing challenging issues and future promises in multi-label active learning with a focus on four core aspects: example dimension, label dimension, annotation, and application extension.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.