Recent Advances in Label Distribution Learning

Topic Information

Dear Colleagues,

Label distribution learning (LDL) is a novel learning paradigm that introduces label distribution to describe the labeling information of one instance. Label distribution defines the relative importance degrees of all labels and is therefore well suited for machine learning problems with label ambiguity. In addition, label enhancement (LE) enables the application of LDL to binary labeled data by automatically recovering label distributions from binary labels, extending the applicability of LDL. Since LDL has found extensive applications in various fields, its advances have garnered widespread attention among the machine learning community. In this Special Issue “Recent Advances in Label Distribution Learning”, we would like to invite researchers to submit their works on the recent advances of LDL, including theory, methodology, applications, and beyond.

Prof. Dr. Xin Geng
Dr. Ning Xu
Prof. Dr. Liangxiao Jiang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	5.0	6.9	2020	20.7 Days	CHF 1600	Submit
Computers computers	4.2	7.5	2012	16.3 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Information information	2.9	6.5	2010	18.6 Days	CHF 1800	Submit
Machine Learning and Knowledge Extraction make	6.0	9.9	2019	25.5 Days	CHF 1800	Submit
Signals signals	2.6	4.6	2020	22.9 Days	CHF 1200	Submit

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Published Papers (1 paper)

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All AI Computers Electronics Information MAKE Signals

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19 pages, 7529 KB  

Open AccessFeature PaperArticle

LCB-Net: Long-Range Context and Box Distribution Network for Small Object Detection

by Yiguo Qiao, Yun Liang and Mingzhe Liu

Electronics 2025, 14(22), 4487; https://doi.org/10.3390/electronics14224487 - 17 Nov 2025

Viewed by 452

Abstract

Small object detection (SOD) remains a critical challenge in computer vision, with vital applications in areas like UAV inspection, autonomous driving, and medical image analysis. Existing methods are often limited by inadequate feature representation for small objects and insufficient utilization of contextual information. [...] Read more.

Small object detection (SOD) remains a critical challenge in computer vision, with vital applications in areas like UAV inspection, autonomous driving, and medical image analysis. Existing methods are often limited by inadequate feature representation for small objects and insufficient utilization of contextual information. To tackle these issues, this paper proposes a novel LCB-Net. First, we design a plug-and-play Saliency-guided Long-range Mamba (SL-Mamba) module, which leverages spatially attentive maps from shallow features to explicitly guide the model’s focus toward small target regions. This module captures long-range contextual dependencies through state space modeling and enhances local–global feature synergy via cross-scale fusion. Second, we introduce a Bounding Box Distribution Loss (BDL) that employs label distribution learning (LDL) to explicitly model localization ambiguity and improve accuracy. Extensive experiments on standard small object benchmarks such as VisDrone, WiderPerson, and NWPU-VHR-10 demonstrate that our approach achieves significant performance gains over strong baselines. Specifically, on the VisDrone dataset, it yields a 4.3% improvement in mAP@0.5:0.95. Furthermore, evaluations across small object benchmarks and the general-purpose MS-COCO dataset confirm that the proposed BDL consistently surpasses traditional IoU-based losses, including CIoU and ProbIoU, in localization tasks. Full article

(This article belongs to the Topic Recent Advances in Label Distribution Learning)

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