15 accepted papers
Accurate spatio-temporal forecasting requires modeling interactions across multiple spatial and temporal scales. Existing Graph Neural Network (GNN) forecasters primarily operate at a single local scale, limiting their ability to capture global processes that govern system dynamics. Hierarchical GNN
Continual learning (CL) aims to continuously learn new tasks from data streams. While most CL research focuses on mitigating catastrophic forgetting, memorizing outdated knowledge can cause negative transfer, where irrelevant prior knowledge interferes with new task learning and impairs adaptability…
Learning from label-limited streams presents significant challenges, particularly when coupled with concept drift and dynamic class imbalance. Existing works often struggle to maintain a discriminative feature space under these constraints, biasing decision boundaries toward majority classes or outd
Motion transfer has emerged as a promising direction for controllable video generation, yet existing methods largely focus on single-object scenarios and struggle when multiple objects require distinct motion patterns. In this work, we present FlexiMMT, the first implicit image-to-video (I2V) motion
Deep neural networks often exhibit degraded performance under domain shifts due to reliance on domain-specific features. Existing domain generalization (DG) methods attempt to mitigate this during training but lack mechanisms to adaptively correct domain-specific reliance once it emerges. We propose…
Recent research has demonstrated the effectiveness of knowledge distillation in Domain Generalization. However, existing approaches often overlook domain-specific knowledge and rely on an offline distillation strategy, limiting the effectiveness of knowledge transfer. To address these limitations, w
Vision-language models (VLMs) have demonstrated strong zero-shot inference capabilities but may exhibit stereotypical biases toward certain demographic groups. Consequently, downstream tasks leveraging these models may yield unbalanced performance across different target social groups, potentially r
Traditional image segmentation methods struggle with fine-grained pattern extraction, especially in an unsupervised setting without labeled data. Shallow and deep learning approaches either lack structural coherence or focus on object-level segmentation rather than internal textures. Additionally, e…
Low-rank adaptation (LoRA) is an efficient strategy for adapting latent diffusion models (LDMs) on a private dataset to generate specific images by minimizing the adaptation loss. However, the LoRA-adapted LDMs are vulnerable to membership inference (MI) attacks that can judge whether a particular d…
Global Climate Models (GCMs) simulate low resolution climate projections on a global scale. The native resolution of GCMs is generally too low for societal-level decision-making. To enhance the spatial resolution, downscaling is often applied to GCM output. Statistical downscaling techniques, in par…
In an era where machine learning permeates every facet of human existence, and data evolves incessantly, the application of machine learning models transcends mere data processing. It involves navigating constant changes exemplified by the phenomenon of concept drift, which often affects model perfo…
Inland water quality monitoring is vital for clean water access and aquatic ecosystem management. Remote sensing machine learning models enable large-scale observations, but are difficult to train due to data scarcity and variability across many lakes. Multi-task learning approaches enable learning…
Deep learning methods often suffer performance degradation due to domain shift, where discrepancies exist between training and testing data distributions. Domain generalization mitigates this problem by leveraging information from multiple source domains to enhance model generalization capabilities…
New Zealand's unique ecosystems face increasing threats from climate change, impacting biodiversity and posing challenges to safety, livelihoods, and well-being. To tackle these complex issues, advanced data science and artificial intelligence techniques can provide unique solutions. Currently, in…
Conditional Density Estimation (CDE) has wide-reaching applicability to various real-world problems, such as spatial density estimation and environmental modelling. CDE estimates the probability density of a random variable rather than a single value and can thus model uncertainty and inverse proble…