CFP: Pattern Recognition Letters - Pattern recognition in multimodal information analysis: observation, extraction, classification, and interpretation, September 2024
Mario Molinara
Pattern Recognition Letters
MOTIVATIONSIn the information age, we grapple with diverse data types like text, images, audio, and video. AI's strides in single-modal analysis are notable, but the challenge lies in efficiently handling massive multimodal data to enhance machines' understanding of the world through pattern recognition. Advancements, in this area have led to techniques. For example, image matching in scenarios involving modes is crucial in diagnostics, remote sensing, and computer vision. Coordinating the retrieval of data from modes improves pattern recognition accuracy, while integrating audio-video data enhances speech recognition and strengthens accident monitoring capabilities. In other words, multimodal learning and representation yield convincingly better results with confidence. However, challenges still need to be addressed, such as handling data types, transforming data effectively, enhancing datasets, and ensuring models' interpretability.
In this context, this special issue outlines recent advances in the pattern recognition field, intending to bring together the work of scholars in this multidisciplinary subject, drawing on the different skills and knowledge of pattern recognition approaches applied in the multimodal information analyzing from the perspective of observing, extraction, classifying and interpretation.
Topics
Multimodal recognition and learning applications
AI-enabled multimedia and multimodal applications
AI-based multimodal detection, retrieval, fusion, analysis, and recommendation
Multimodal information cooperative processing and recognition
Recognition, classification, and analysis of multimodal information
Deep learning approaches for pattern recognition in multimodal information analysis
Unsupervised/self-supervised approaches in modality alignment
Novel multimodal representation models for image (RGB-D, RGB-T) and video domains
Feature extraction, fusion, and observation of cross-modal information
Promotion of single-modal information recognition through multimodal information fusion
Multimodal representation learning algorithm based on AI and PR.
Guest editors Jingsha He, PhD Danilo Avola, PhD | KC Santosh, PhD Mario Molinara, PhD Daniele Salvati, PhD |
Important dates | Submission Portal Open: September 1st, 2024 Submission Deadline: September 20th, 2024 Acceptance Deadline: December 15th, 2024 |
For inquiries regarding the special issue, send an email to the managing guest editor at: j...@bjut.edu.cn