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Found 4 matching student topics

Displaying 1–4 of 4 results

Multi-modal sentiment analysis

In deep learning models, language models and word embedding methods have become popular to understand the context of text data. Popular language models such as BERT have limitations in terms of the token length. There exist some corpora that have longer text with an average of 1000 tokens. Additionally, these corpora are text-heavy and only include some images.In our prior works, we have developed several multi-modality models on social media datasets.

Study level
Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Computer Science
Research centre(s)
Centre for Data Science

Evaluation of language models and word embedding methods for natural language processing applications

In deep learning models, language models and word embedding methods have become popular to understand the context of text data. There exist many variants of these methods and have different limitations. This project will introduce you to the hot topic of language models and the fields of Natural Language Processing and Text Mining. 

Study level
Honours
Faculty
Faculty of Science
School
School of Computer Science
Research centre(s)
Centre for Data Science

Enhancing 3D visual understanding through multimodal data fusion

The demand for 3D scene understanding through point clouds is rapidly growing in diverse applications, including augmented and virtual reality, autonomous driving, robotics, and environment monitoring. However, the field faces challenges due to limited data availability and predefined categories. Training deep 3D networks effectively for sparse LiDAR point clouds requires significant amounts of annotated data, which is both time-consuming and expensive. Building on the advancements in 2D models that leverage the power of image and language knowledge, our project aims …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics

Interactive (and collaborative) robot programming using language (Project 2.5 - Joint CSIRO/ACC)

Programming robots to carry out desired tasks is difficult and time-consuming. This PhD project focuses on collaborative and instructional dialogue agents to help human operators program robot tasks.In this collaborative scenario, a human operator converses with an AI agent to explain the steps that are to be performed, using high-level references and abstractions that make sense to the human, as opposed to simple verbal instructions corresponding to rudimentary robot movements. The AI agent must interpret the high-level instructions and translate …

Study level
PhD
Faculty
Faculty of Creative Industries, Education and Social Justice
School
School of Design
Research centre(s)

Design Lab

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