## 1 Introduction  
Air cruiser is a task actuator that can operate continuously in a wide space area for a long time with high mobility. The implementation of video instance segmentation based on the vision system to carry out object detection [1, 2, 3], segmentation [4, 5, 6, 7], and tracking [8, 9, 10] can provide decision-making basis for the accurate localization of target and autonomous flight navigation. Video instance segmentation is an important extension of instance segmentation task in the video field. With the introduction of MaskTrack R-CNN [11], the task of video instance segmentation has been completely realized, making it gradually become a hotspot in computer vision research [12, 13, 14, 15]. MaskTrack R-CNN improves on the two-stage instance segmentation network Mask R-CNN, where a new tracking branch is introduced to solve the subtask of instance association in video processing. Subsequently, many video instance segmentation methods were derived [16, 17, 18, 19, 20], which significantly improved the accuracy and speed compared with MaskTrack R-CNN. Recently, due to the success of DETR [21] in the field of video object segmentation, VisTR [22], SeqFormer [23], Mask2 Former [24], and other methods took advantage of DETR architecture and build Transformer [25]-based cross attention to deal with 3D spatio-temporal embedding, which significantly improved the segmentation accuracy of full supervised video instances in a clip-level paradigm. However, clip-level video instance segmentation method needs to process the complete video in a single training or inference and provide pixel-level instance annotation for each frame. The long-time video samples not only result in a huge amount of computation but also need to consume a lot of time for manual fine annotation, especially for the vision system of the air cruiser. CrossVIS [26] is a single-stage CNN-based algorithm that reaches real-time inference on a consumer-grade GPU. CrossVIS uses images of different frames of the video for cross-learning between the frames to achieve accurate and stable online instance association. Based on the mask dynamic convolution of CondInst [27], the cross-prediction mask is generated by cross-combining the mask features of different frames and the instance dynamic parameters. The mask prediction part is constrained by the calculation loss of the cross prediction mask and the current frame ground truth mask, which effectively realizes the cross-frame learning of the relationship from the target instance to the pixel level and achieves a good balance between speed and accuracy. Although the video instance segmentation network is constantly updated, the annotation of mask labels in the video instance segmentation datasets is still a great challenge. Current weakly supervised networks for video instance segmentation are mainly based on image-level or box-level annotation. Most image-level methods are based on class activation map (CAM) [28] and use the relationship between pixels to generate pseudo-labels, which are further refined later. However, CAM usually cannot detect the precise boundary of the whole object region and leads to a large gap between the predicted mask and ground truth. On the basis of IRNet [29], [PERSON] et al. [30] propose FlowIRN and MaskConsist modules to extend it to weakly supervised video instance segmentation, which alleviates the problems of missing targets. Compared with the image-level task, the box-level task generates a mask prediction constraint function based on the bounding box label to supervise the training more effectively. STC-Seg [31] uses unsupervised depth estimation and optical flow complementation to generate effective pseudo-labels, and uses the pseudo-labels as supervision to constrain the prediction mask. The two methods additionally introduce other training consumption and cannot achieve end-to-end network training, limiting the training efficiency of weakly supervised methods.