Introduction to the Special Issue on Cognitive-Inspired Multimedia Information Processing and Applications for Low-Resource Languages

Introduction to the Special Issue on Cognitive-Inspired Multimedia Information Processing and Applications for Low-Resource LanguagesIn recent years, there has been a growing recognition of the importance of preserving and promoting linguistic diversity.Low-resource languages, often spoken by marginalized communities, face numerous challenges in accessing and utilizing multimedia information effectively.However, advancements in cognitive-inspired approaches offer promising avenues to address these challenges.Cognitive-inspired methods draw inspiration from human cognition and perception, seeking to replicate or augment cognitive processes in computational systems.By leveraging these approaches, researchers aim at developing multimedia information processing techniques that are more robust, adaptive, and effective, particularly in the context of low-resource languages.In this special issue, we received seven articles that underwent a complete double-blind peer review.Based on the review process, one article has been accepted for this special issue.Below is an introduction to the accepted article and highlights its major contributions.In the research article titled "Feature Fusion Pyramid Network for End-to-end Scene Text Detection " the author proposes the Feature Fusion Pyramid Network for end-to-end scene text detection, which integrates multi-modal information.This is achieved by dividing the pyramid structure into high-level and low-level layers, and employing channel and spatial attention modules to enhance feature representation at both levels.Experimental results demonstrate the effectiveness of the proposed method, with precision and recall rates reaching 88.7%/82.1%,77.0%/60.3%, and 85.3%/74.8% on the ICDAR2015, ICDAR2017-MLT, and MSRA-TD500 datasets, respectively.This innovative approach represents a significant step forward in advancing the accuracy and reliability of scene text detection in computer vision applications.We extend our sincere gratitude to all the authors, reviewers, and editors whose contributions have made this special issue possible.