## 1 Introduction
There are few studies on weakly supervised video instance segmentation, but the research on weakly supervised image instance segmentation [29; 32; 33; 34; 35] represented by BoxInst [32] has made remarkable progress. BoxInst [32] redesigns the loss of the mask learning part of the instance segmentation network without modifying the network. The new losses can supervise the mask training without relying on the mask labels, effectively utilizing weak supervision and avoiding additional training consumption. Currently, most weakly supervised video instance segmentation methods obtain rough instance mask segmentation results relying on weak box-level annotation to constrain instance locations and use the additional supervised signal to achieve pixel-level semantic classification. However, the inherent scale variability across instances (e.g., human vs. cars) and intra-instance scale changes caused by perspective shifts result in limited multi-scale features and low mask quality in existing weakly VIS frameworks. Therefore, this paper network combines CrossVIS [26] with the BoxInst weakly supervised paradigm and proposes a novel weakly supervised video instance segmentation method based on multi-scale location and grouping weighting. The main contributions of this paper are as follows. Firstly, the high-level features of Feature Pyramid Networks (FPNs) in CrossVIS network undergo multiple down-sampling operations, resulting in the lack of spatial location information in the structure of FPN and weak correlation between cross-depth features. In this paper, a spatial information refinement module (SIR) is added between the backbone network and FPN, which maps the low-level features of the backbone network to the high-level features of FPN. It enhances the correlation between the output features of FPN and the low-level feature set so as to solve the problems of rough spatial location information and weak correlation between cross-depth features. Secondly, the output of FPN is used as the input feature map of the mask branch in CrossVIS. The feature maps of the lower three layers of FPN are upsampled and added as the input of the mask branch, and then the mask branch features are used through multiple convolutional layers. In order to solve the problem of the loss of deep semantic information caused by the sharp drop of the mask branch feature channel, the feature map of the lower three layers of FPN is upsampled and added as the input of the mask branch. MLGW-VIS uses the group weighting module after the mask branch, which divides the mask branch features into multiple groups. The mask branch features map the features of a single subspace so that it can undergo multi-scale and multi-frequency feature learning and enhance the feature representation ability in each sub-channel. The problem of incomplete extraction of branch information from the original mask is remedied. Further, a projection loss function is generated based on the bounding box information to guide the prediction mask to narrow the gap between its prediction box and the ground truth bounding box, and the color similarity loss function is constructed by using the image semantic information to further constrain the prediction mask so as to complete the weakly supervised video instance segmentation task without changing the basic network structure. Finally, a high-performance air cruiser and remote flight control base station are built while integrating the research methods and practical application scenarios. The accuracy lossless transfer of model across platforms is realized through the segmentation model transformation framework, and the post-processing form program of model reasoning results is designed to complete the visualization output of instance segmentation of video samples and the localization coordinates of objects.