- Semantic Instance Segmentation for Information Retrieval:
This theme focuses on developing semantic instance segmentation techniques for multimedia information retrieval. It involves utilizing deep learning and computer vision approaches to accurately identify and segment objects or regions of interest in multimedia data. - Multimedia Document Classification and Summarization:
This themec explores the area of multimedia document classification and summarization. It involves developing algorithms and models to automatically categorize and summarize multimedia documents such as images, videos, and audio files, enabling efficient retrieval and understanding of large multimedia collections. - Geographic Visual Representation and Spatial Information Retrieval:
This theme focuses on geographic visual representation and spatial information retrieval. It involves developing techniques to represent and analyze spatial information in multimedia data, enabling efficient retrieval and exploration of geographically related content. - Knowledge Graph for Information Representation and Retrieval:
This theme explores the use of knowledge graphs for information representation and retrieval. It involves constructing and utilizing knowledge graphs to capture and organize structured information, enabling more precise and context-aware retrieval of multimedia data. - Augmented Extended Reality for Information Visualization and User Interaction:
This theme investigates the design and the implementation of extended reality applications in information visualization and user human machine interaction. It involves designing immersive and interactive augmented extended reality systems that enhance the presentation and the exploration of multimedia information, enabling intuitive user interactions. - Multimodal Content-Based Information Retrieval:
This thesis research topic focuses on multimodal content-based information retrieval. It involves developing algorithms and techniques to retrieve multimedia data based on its content, considering multiple modalities such as text, image, audio, and video, enabling more comprehensive and accurate retrieval results. - Multimedia Focused Crawler:
This theme explores the development of a multimedia-focused crawler. It involves designing and implementing crawling algorithms and strategies specifically tailored for efficiently discovering and retrieving multimedia content from the web. - Semantic Indexing:
This theme investigates semantic indexing techniques for multimedia information retrieval. It involves developing methods to automatically extract and associate semantic information with multimedia data, enabling more precise indexing and retrieval based on semantic concepts. - Multimedia Semantic Matching for Very Large Knowledge Bases:
This theme focuses on multimedia semantic matching for very large knowledge bases. It involves developing scalable algorithms and techniques to match and relate multimedia data with vast knowledge bases, enabling efficient and accurate retrieval of relevant information. - Very Large Knowledge Bases Visualization:
This theme explores visualization techniques for very large knowledge bases. It involves developing visual representations and interactive interfaces to explore and navigate large-scale knowledge bases, facilitating efficient and effective knowledge discovery and retrieval.
