Applications of Computer Vision
Submission Date: 2021-09-30

Dear Colleagues,

Computer vision (CV) techniques are widely used by the practicing engineer to solve a whole range of real vision problems. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible, covering some practical application of CV methods in all branches of science and engineering. Submitted papers should report some novel aspect of CV use for a real-world engineering application and also validated using data sets. There is no restriction on the length of the papers. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.

The website of the SI is https://www.mdpi.com/journal/electronics/special_issues/Applications_CV

Focal points of the Special Issue include but are not limited to innovative applications of:

    Medical and biological imaging
    Industrial inspection
    Robotics
    Photo and video interpretation
    Image retrieval
    Video analysis and annotation
    Multimedia
    Sensors and more.

Dr. Eva Cernadas
Guest Editor

Special Issue on Special Issue on "Challenges and New Opportunities for Next-Generation Recommender Systems"
截稿日期: 2023-10-15

Dear Colleagues,

Recommender systems rely on users' explicit or/and latent interests to offer personalized suggestions of items automatically, aiming to help users cope with information overload. Despite its indispensable role in the areas of e-commerce, targeted advertising, intelligent medical assistants and online media, recommender systems are still facing many challenges which require a deeper analysis of both the user, the content, and their relationships, such as the context-awareness, the (sequential) user behavior modeling, the explainability, diversity, and fairness of recommender systems, multi-stakeholder recommendation, data sparsity issues as well as misinformation detection. Fortunately, with the advancement of deep learning, multi-modal data have become better accessible for AI technology. The great success of language modelling techniques also inspires recommendation models to alleviate the aforementioned challenges through knowledge transmission and distillation from the pre-trained models.

This special issue solicits the latest and significant contributions on developing and applying efficient user modeling and advanced machine learning techniques for building next-generation recommender systems and particularly on data and model-driven intelligent recommender systems.

Topics of interest include but are not limited to, the following:

1) Cross-domain/source recommendation
2) Context-aware/group/multi-criteria/multi-objective recommender systems
3) Multi-modal recommendation
4) Explainanality/transparency in recommender systems
5) Fairness/bias in recommender systems
6) Efficient and scalable recommendation techniques
7) Pre-trained recommendation model
8) Self-supervised learning for recommendation
9) Data sparsity, cold start and long-tail issues in recommender systems
10) Knowledge transfer and distillation in recommender systems
11) Surveys, reviews and prospects on next-generation recommender systems

Dr. Peng Liu
Dr. Lemei Zhang
Dr. Yong Zheng
Guest Editors