Ieee Transactions on Pattern Analysis and Machine Intelligence
Overview
The IEEE Transactions on Pattern Analysis and Machine Intelligence is a prestigious journal that focuses on the latest advancements in the fields of pattern analysis and machine intelligence. It publishes high-quality research papers that explore innovative techniques, algorithms, and applications in areas such as computer vision, image processing, pattern recognition, and artificial intelligence. The journal serves as a platform for researchers, scientists, and practitioners to exchange knowledge and contribute to the development of cutting-edge technologies in these domains.
Details
Details
Abbr.
IEEE Trans Pattern Anal Mach Intell
Start
1979
End
Continuing
Frequency
Monthly, 1989-
p-ISSN
0162-8828
e-ISSN
1939-3539
Country
United States
Language
English
Specialties
Biomedical Engineering
Medical Informatics
Medical Informatics
Metrics
Metrics
h-index / Ranks: 65
417
SJR / Ranks: 180
6158
CiteScore / Ranks: 150
30.40
JIF / Ranks: 128
23.6
Recent Articles
11.
Hou Z, Yu B, Wang C, Zhan Y, Tao D
IEEE Trans Pattern Anal Mach Intell
. 2025 Mar;
PP.
PMID: 40063428
Despite the great success achieved, deep learning technologies usually suffer from data scarcity issues in real-world applications, where existing methods mainly explore sample relationships in a vanilla way from the...
12.
Zhang R, Meng Q, Zhu R, Wang Y, Shi W, Zhang S, et al.
IEEE Trans Pattern Anal Mach Intell
. 2025 Mar;
PP.
PMID: 40053667
In scenarios with limited available data, training the function-to-function neural PDE solver in an unsupervised manner is essential. However, the efficiency and accuracy of existing methods are constrained by the...
13.
Zhou J, He Z, Zhang D, Liu S, Fu X, Li X
IEEE Trans Pattern Anal Mach Intell
. 2025 Mar;
PP.
PMID: 40048345
Feature drift is caused by the dynamic coupling of target features and degradation factors, which reduce underwater detector performance. We redefine feature drift as the instability of target features within...
14.
Huang J, Liu T, Chen Z, Liu X, Wang M, Dragotti P
IEEE Trans Pattern Anal Mach Intell
. 2025 Mar;
PP.
PMID: 40048344
Single Image Reflection Removal (SIRR) is a canonical blind source separation problem and refers to the issue of separating a reflection-contaminated image into a transmission and a reflection image. The...
15.
Liu J, Yang C, Lu Z, Chen J, Li Y, Zhang M, et al.
IEEE Trans Pattern Anal Mach Intell
. 2025 Mar;
PP.
PMID: 40048343
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains. Meanwhile, the field of...
16.
Choi J, Lee S, Park H, Jung S, Kim I, Cho J
IEEE Trans Pattern Anal Mach Intell
. 2025 Mar;
PP.
PMID: 40048342
In this paper, we propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, SVBRDF, and 3D spatially-varying lighting. While multi-view images have been...
17.
Ying X, Xiao C, An W, Li R, He X, Li B, et al.
IEEE Trans Pattern Anal Mach Intell
. 2025 Mar;
PP.
PMID: 40042962
Visible-thermal small object detection (RGBT SOD) is a significant yet challenging task with a wide range of applications, including video surveillance, traffic monitoring, search and rescue. However, existing studies mainly...
18.
Harell A, Foroutan Y, Ahuja N, Datta P, Kanzariya B, Somayazulu V, et al.
IEEE Trans Pattern Anal Mach Intell
. 2025 Mar;
PP.
PMID: 40042961
Recent years have seen a tremendous growth in both the capability and popularity of automatic machine analysis of media, especially images and video. As a result, a growing need for...
19.
Nguyen D, Nguyen L, Huang Y, Pham C, Hoai M
IEEE Trans Pattern Anal Mach Intell
. 2025 Mar;
PP.
PMID: 40042960
We present Class-agnostic Repetitive action Counting (CaRaCount), a novel approach to count repetitive human actions in the wild using wearable devices time series data. CaRaCount is the first few-shot class-agnostic...
20.
Bao Z, Yao P
IEEE Trans Pattern Anal Mach Intell
. 2025 Mar;
PP.
PMID: 40042959
We consider the problems of testing and learning quantum -junta channels, which are -qubit to -qubit quantum channels acting non-trivially on at most out of qubits and leaving the rest...