Call for Papers

Recently, explosive amount of visual content has been acquired with different kinds of visual sensors such as surveillance cameras, mobile phones, medical imaging equipment and remote sensors. The existing sensors may not always provide enough content or sufficient quality for different semantic analysis tasks. How to enhance the quality of the available visual data and reconstruct more additional information with computational technique such as hyper-spectral image reconstruction and high-speed video reconstruction from a snapshot have great affect for the subsequent vision tasks. Furthermore, the automatically/quantitatively analysis and understanding of the available visual data without sufficient quality is becoming one of the most active research areas in the vision community due to the scientifically challenging problems and its great benefits to real life applications. On the other hand, machine learning techniques especially the deep learning framework have manifested the surprising superiority for extracting structural and semantic visual representation in numerous computer vision applications such as image classification, object detection/localization, image segmentation, captioning, and so on. With machine learning and computing techniques, it is prospected to discover the inherent structure of the available unconditioned visual contents and to achieve more promising results for various applications based on visual semantic analysis.

This workshop, on Machine Learning and Computing for Visual Semantic Analysis (MLCSA2024) – aims at sharing latest progress and developments, current challenges, and potential applications for exploiting large amounts of visual contents. We are interested in constructing effective systems to enable visual semantic analysis and building wide applications within the fields of artificial intelligence, machine learning, ubiquitous computing, data mining, and others.

Topics

The topics we are interested in, include constructing effective systems to enable visual semantic analysis and building wide applications within the fields of artificial intelligence, machine learning, image processing, ubiquitous computing, data mining, and others.