This minitrack explores the role of computer vision and machine learning models in analyzing and interpreting visual data across diverse domains. With the rapid advancements in deep learning architectures and multimodal analytics, computer vision has become a critical tool in automating decision-making, enhancing operational efficiencies, and enabling intelligent systems.
The session invites research contributions that push the boundaries of computer vision applications, from traditional tasks such as image classification and object detection to emerging fields like object tracking, spatial analytics, and real-time video processing. Submissions should emphasize the innovations, implementations, and interdisciplinary impacts of computer vision in fields including logistics, healthcare, geospatial intelligence, industrial automation, and security.
Here is a general list of topic areas for this minitrack:
1. Image and Video Analysis – Advancements in image classification, object detection, segmentation, including applications.
2. Geospatial and Environmental Vision Applications – Use of computer vision for satellite imaging, urban planning, environmental monitoring, and disaster response.
3. AI-Powered Industrial and Business Solutions – Applications in manufacturing, logistics, quality control, supply chain management, and predictive maintenance.
4. Multimodal and Generative AI – Integrating computer vision with NLP, sensor data, and generative models for enhanced analytics, automation, and creativity.
5. Real-Time and Edge AI Deployment – Implementing vision models in real-time, including autonomous vehicles, IoT devices, robotics, and augmented/virtual reality.
6. Ethical and Fair AI in Computer Vision – Addressing challenges in fairness, bias mitigation, privacy, and responsible deployment of vision-based AI.
7. Innovative and Emerging Applications – Novel use cases of computer vision across diverse fields such as healthcare, finance, agriculture, and smart cities.