The University of Adelaide is a research-intensive Australian university renowned for its excellence in science, engineering, and wine technology. It leads collaborative innovation in health, sustainability, and AI. The University of Adelaide and NECLA co-developed strategies to exploit unlabeled data for vision-and-language learning. We introduced research methods that efficiently harness pre-trained models, helping to bridge the gap between limited supervision and real-world AI needs in multimodal tasks.

Posts

THAT: Token-wise High-frequency Augmentation Transformer for Hyperspectral Pansharpening

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Transformer-based methods have demonstrated strong potential in hyperspectral pansharpening by modeling long-range dependencies. However, their effectiveness is often limited by redundant token representations and a lack of multiscale feature modeling. Hyperspectral images exhibit intrinsic spectral priors (e.g., abundance sparsity) and spatial priors(e.g., non-local similarity), which are critical for accurate reconstruction. From a spectral–spatial perspective, Vision Transformers (ViTs) face two major limitations: they struggle to preserve high-frequency components—such as material edges and texture transitions, and suffer from attention dispersion across redundant tokens. These issues stem from the global self-attention mechanism, which tends to dilute high-frequency signals and overlook localized details. To address these challenges, we propose the Token-wise High-frequency AugmentationTransformer (THAT), a novel framework designed to enhance hyperspectral pansharpening through improved high-frequency feature representation and token selection. Specifically, THAT introduces: (1) Pivotal Token Selective Attention (PTSA) to prioritize informative tokens and suppress redundancy; (2) a Multi-level Variance-aware Feed-forward Network (MVFN) to enhance high-frequency detail learning. Experiments on standard benchmarks show that THAT achieves state-of-the-art performance with improved reconstruction quality and efficiency.

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Scalable Deep k-Subspace Clustering

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Subspace clustering algorithms are notorious for their scalability issues because building and processing large affinity matrices are demanding. In this paper, we introduce a method that simultaneously learns an embedding space along subspaces within it to minimize a notion of reconstruction error, thus addressing the problem of subspace clustering in an end-to-end learning paradigm. To achieve our goal, we propose a scheme to update subspaces within a deep neural network. This in turn frees us from the need of having an affinity matrix to perform clustering. Unlike previous attempts, our method can easily scale up to large datasets, making it unique in the context of unsupervised learning with deep architectures. Our experiments show that our method significantly improves the clustering accuracy while enjoying cheaper memory footprints.

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