SPAN 25

Intelligent Applications for Industrial Systems

(SPAN)

The Saliency Prediction in Action (SPAN) workshop is a satellite event at the International Joint Conference on Neural Networks (IJCNN), focusing on applications of saliency prediction in a wide variety of industrial domains. This workshop examines advanced methodologies in machine learning and cognitive neuroscience that enable intelligent systems to replicate human-like attention mechanisms. Saliency prediction plays a critical role in a wide range of applications, including autonomous vehicles, smart cities, healthcare, and media, where efficient attention modeling enhances decision-making and system performance. By bridging cognitive theories of attention with neural network research, the workshop emphasizes biologically inspired methods and their relevance in industrial settings. The event facilitates discussions among researchers and industry professionals, providing a platform for exploring the latest advancements and fostering collaborations that enhance the practical impact of neural networks in various sectors. Contributions from academics, industry practitioners, students, and experts are encouraged, aiming to advance the field through innovative research, applied solutions, and interdisciplinary perspectives.

Note: Submitted papers should be up to 8 pages according to IJCNN style and upon acceptance, the papers will be published in the IJCNN 2025 proceedings.

For any question, send a message to mort.moradi@gmail.com

Topics of Interest

Saliency prediction in real-world applications, including remote sensing, underwater explorations, etc.
Saliency-guided robot navigation and collaboration in social, industrial and smart environments.
Multimodal saliency prediction for video comprehension in ego-centric settings, autonomous vehicles scenarios and so on.
Driver visual attention monitoring and autonomous vehicles.
Human attention modeling and neuroscience.
Environmental and ambience-related effects on human visual attention and saliency prediction.
Gaze estimation for emotion and intention prediction in intelligent environments.
Image and video saliency prediction models.
New trends and concepts in saliency prediction using large models.
Concepts and future directions of human visual attention modelling.