In Multimodal Learning, the focus is on developing systems that can seamlessly integrate
information from diverse data types such as text, images, and audio. By using the power
of multiple modalities, our AI models aim to enhance comprehension, enabling more
robust and versatile solutions across various applications.
a. Single-Branch Networks for Multimodal Training
In this study, we aim to address the huge volume of multimedia content on social
media by proposing single-branch networks. This approache is designed to
adeptly learn discriminative representations for both unimodal and multimodal
tasks, introducing a transformative paradigm for handling audio, images, and text
seamlessly. A pivotal aspect of our research lies in developing versatile
approaches and algorithms that empower the single-branch network to undergo
training with either single or multiple modalities, ensuring optimal performance
without compromise.
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b Cross-Model Verification and Recognition
In this study, we introduce a challenging task focused on establishing associations
between faces and voices across various languages spoken by the same individual.
The primary objective is to address pivotal inquiries: "Will face-voice association
proves to be language independent?" and "Can a speaker be reliably recognized
regardless of the spoken language?". These inquiries hold significant importance
for comprehending the efficacy of the technology and will guide efforts to
advance the development of multilingual biometric systems. Our future
perspective aims to unravel the intricate dynamics of cross-language face-voice
associations.
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c. Multimedia Learning Using Conceptual Graph
In this study, our focus lies in the exploration of multimedia text-to-picture
mobile learning systems based on conceptual graph matching. While multimedia
learning involves constructing mental representations from words associated with
images, the current challenge involves the labor-intensive and time-consuming
manual work required for pedagogic illustrations. Conventional systems relying
on optimal keyword and sentence selection may encounter information loss. We
aim to address these limitations by investigating innovative approaches that
leverage conceptual graph matching, paving the way for more effective and
efficient multimedia learning systems on mobile platforms.
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