Write in a clean editor, then format for Machine Intelligence Research in one click — DocuGuru applies the official Springer Nature template with superscript references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
Machine Intelligence Research is a peer-reviewed journal published by Springer Nature, covering Advanced Neural Network Applications, Multimodal Machine Learning Applications, Topic Modeling.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. Machine Intelligence Research 12, 45–58 (2023).
Formats any DOI in Machine Intelligence Research style. No sign-up. |
| Publishes research in | Advanced Neural Network Applications Multimodal Machine Learning Applications Topic Modeling Domain Adaptation and Few-Shot Learning Advanced Image and Video Retrieval Techniques |
| ISSN | 2731-538X |
| Citation impact (2-yr) | 7.84 |
| h-index | 35 |
| i10-index | 97 |
| Total citations | 5,561 |
| Top institutions publishing here | Chinese Academy of Sciences |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making reasonable decisions while driving. We present a panoptic driving perception network (you only look once for panoptic (YOLOP)) to perform traffic object detection, drivable area segmentation, and lane detection simultaneously.…
Abstract The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the promising setting…
Abstract This paper introduces deep gradient network (DGNet), a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from…
Abstract In the past few years, the emergence of pre-training models has brought uni-modal fields such as computer vision (CV) and natural language processing (NLP) to a new era. Substantial works have shown that they are beneficial for downstream uni-modal tasks and avoid training a new model from scratch. So can such pre-trained models be…
Abstract This paper aims to address the problem of supervised monocular depth estimation. We start with a meticulous pilot study to demonstrate that the long-range correlation is essential for accurate depth estimation. Moreover, the Transformer and convolution are good at long-range and close-range depth estimation, respectively. Therefore, we propose to adopt a parallel encoder architecture…
