In today's data-driven era, the explosive growth of global data volume has led to an increasing consumption of computing and storage resources. Effective management of virtual machines (VMs) memory usage is critical for cloud vendors to optimize system performance and resource utilization. Existing memory prefetching methods often slow down system performance, creating a difficult balance between maintaining service quality and optimizing resource use. For instance, Leap, which primarily utilizes address information, performs poorly in VM environments. The main issue is the performance drop caused by the reuse of memory resources in virtualized environments, a common situation in public clouds.

Pre-training and fine-tuning have been the de-facto paradigm in vision-language domains. Along with the rapid growth of model sizes, fully fine-tuning these large-scale vision-language pre-training (VLP) models requires prohibitively expensive storage costs. To address this issue, recent advances in NLP offer a promising and efficient adaptation approach called LoRA, which aims to approximate the fine-tuning of large pre-trained model by updating low-rank parameters. Despite its effectiveness, we identify that LoRA suffers a large approximation error on VLP models and its optimization is also inefficient, which greatly limits its performance upper bound. In this paper, we mathematically prove that the approximation error of low-rank adaptation can be optimized by a new optimization objective, i.e., the weight distance between LoRA and fine-tuning. Based on this finding, we propose a novel PETL method for VLP models, namely momentum imitation learning (MoIL). Specifically, MoIL formulates PETL as a weight imitation learning process and directly optimize the approximation error bound of the low-rank adaptation. Based on this training scheme, we also explore a new hybrid approximation function to reduce the learning difficulty of low-rank adaptations. With these two novel designs, MoIL can greatly improve the optimization efficiency of the low-rank parameters on VLP models. We validate MoIL on three VLP models ranging from end-to-end network to two-stage network, and conduct extensive experiments on four VL tasks. Experimental results demonstrate superior performance and optimization efficiency of MoIL than existing PETL methods. For instance, by updating only 6.23% parameters, MoIL can even outperform full tuning by +2.3% on image-text matching task. Meanwhile, its inference efficiency and generalization ability is also validated by multiple VLP models, e.g., VLMO and VinVL.

Referring Expression Segmentation (RES) is a challenging task in computer vision that involves segmenting image instances using textual descriptions. Conventional approaches suffer from the high cost of acquiring segmentation labels. To overcome this, we propose a novel learning task called Omni-supervised Referring Expression Segmentation (Omni-RES) which leverages unlabeled, fully labeled, and weakly labeled data, such as referring points or bounding boxes, for efficient RES training. Our approach is based on a teacher-student learning framework, where weak labels guide the selection and refinement of high-quality pseudo-masks for training, rather than serving as direct supervision signals. We tested Omni-RES on various state-of-the-art RES models and datasets, demonstrating its superiority over fully-supervised and semi-supervised methods. Remarkably, with just 10% fully labeled data, Omni-RES can match the performance of 100% supervised training. Additionally, it enables using large-scale vision-language datasets like Visual Genome for cost-effective RES training, setting a new state-of-the-art performance in RES, such as 80.66 on RefCOCO. Our code is released at: https://github.com/nineblu/omni-res