The rapid advancement of Large Language Models (LLMs) and Large Vision Models (LVMs) has achieved breakthrough performance but has also created significant deployment challenges due to their massive scale, often exceeding 100 billion parameters. Parameter-Efficient Fine-tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), address this issue by approximating weight updates as products of low-rank matrices, dramatically reducing the number of trainable parameters while maintaining competitive performance. However, LoRA’s effectiveness is fundamentally constrained by the limited effective rank of its update matrices, limiting its ability to capture the rich, high-dimensional changes required for complex adaptation tasks. We propose GroupLoRA, which overcomes this limitation through a divide-and-conquer strategy that partitions weight matrices into g groups, with independent LoRA modules for each group. GroupLoRA introduces two key innovations: (1) an Inter-Group Bridge module that enables information exchange between groups through learnable interaction matrices, and (2) Learnable Scaling Factors that dynamically adjust each group’s contribution to the final output. This design enables group specialization while maintaining cross-group coordination, maximizing model expressiveness within limited parameter budgets. Extensive experiments on LLMs and VLMs demonstrate that GroupLoRA consistently achieves higher performance with fewer parameters than conventional LoRA. Our approach successfully applies group-wise processing principles to parameter-efficient fine-tuning, offering a practical solution for efficient large-model adaptation across diverse tasks.

Multilabel remote sensing image classification is a fundamental task that classifies multiple objects and land covers within an image. However, training deep learning models for this task requires a considerable cost of labeling. While several efforts to reduce labeling costs have been made, they often result in decreased label quality and the inclusion of incorrect (i.e., noisy) labels. To tackle this issue, algorithms for training deep learning models robust to multilabel noise have been proposed in the literature. Nonetheless, the efficacy of these algorithms has been evaluated only under instance-independent multilabel noise, where noise is generated regardless of the individual characteristics and features of each remote sensing image. In this article, we introduce generating instance-dependent multilabel noise into multilabel remote sensing image datasets for the first time. We leverage a vision-language model with zero-shot prediction capabilities to compute categorywise prediction scores for each image, based on which we generate multilabel noise in an instance-dependent manner. We demonstrate that the proposed instance-dependent multilabel noise is more feasibly generated with respect to individual images compared to traditional instance-independent multilabel noise. We also demonstrate that a more challenging noise scenario is generated, which leads to a more complex decision boundary and stronger overfitting during deep learning model training. Finally, we re-evaluate existing noise-robust training algorithms under the generated instance-dependent multilabel noise and observe that several algorithms exhibit limited robustness against instance-dependent multilabel noise.