12 accepted papers
Adapting large, adversarially pre-trained models to specialized domains via transfer learning is a promising path toward building secure AI systems. However, a critical challenge arises when fine-tuning on limited downstream data: models often suffer from catastrophic forgetting of robustness, where
Spiking Federated Learning (SFL) has been widely studied with the energy efficiency of Spiking Neural Networks (SNNs). However, existing SFL methods require model homogeneity and assume all clients have sufficient computational resources, resulting in the exclusion of some resource-constrained clien
The temporal evolution patterns of surface spatial structures constitute a central concern within the field of intelligent remote sensing interpretation.However, constrained by the availability of only two temporal phases, modeling sparse spatio-temporal change processes to effectively interpret sur
Large language models (LLMs) are increasingly adapted to downstream tasks in resource-constrained scenarios, making quantization-aware fine-tuning (QAF) a common practice for practical deployment. However, we find that quantized LLMs are substantially more vulnerable to safety alignment degradation …
The increasing scale of graph datasets has significantly improved the performance of graph representation learning methods, but it has also introduced substantial training challenges. Graph dataset condensation techniques have emerged to compress large datasets into smaller yet information-rich data
Few-shot Named Entity Recognition (NER) spotlights the tag of novel entity types in data-limited scenarios or lower-resource settings. Advances with Pre-trained Language Models (PLMs), including BERT, GPT, and their variants, have driven tremendous strategies to leverage context-dependent representa…
Deep learning has been widely applied to various aspects of computer vision, but the emergence of adversarial attacks raises concerns about its reliability. Adversarial training (AT) is one of the most effective defense methods, which incorporates adversarial examples into the training data. However…
Large Language Models (LLMs) have achieved impressive success across a range of natural language processing tasks. However, they still underperform in text classification tasks compared to fine-tuned small models. This can be linked to complexities in addressing context-dependent expressions and com
Personalized federated learning (PFL) is a new paradigm to address the statistical heterogeneity problem in federated learning. Most existing PFL methods focus on leveraging global and local information such as model interpolation or parameter decoupling. However, these methods often overlook the ge…
The metro flow in Urban Rail Transit Systems (URTS) differs from other urban traffic flows because it is characterized by: (1) highly predetermined scheduling; and (2) interactively dynamic dependencies over the fixed physical infrastructure that vary with spatiotemporal and environmental factors. N…
Spectral super-resolution (SSR) from RGB images, which involves reconstructing hyperspectral images (HSIs) from color images, has recently received great attention. While convolutional neural network (CNN)-based methods have demonstrated strong performance, they often overlook the self-similarity ac…
Text summarization is to generate a brief version of a given article while maintaining its essential meaning. Most existing solutions typically relied on the standard attention-based encoder-decoder framework, where each token in the source article, including redundancy, would be contributed to the…