The current deep learning based instance segmentation methods cannot fully train the network model and result in sub-optimal segmentation results due to the lack of labeled engine blade data. To improve the precision of aeroengine blade instance segmentation， an aeroengine blade instance segmentation method based on incomplete instance guidance was proposed. Combining with an existing instance segmentation method and an interactive segmentation method， promising aeroengine blade instance segmentation results were obtained. First， a small amount of labeled data was used to train the instance segmentation network， which generated initial instance segmentation results of aeroengine blades. Secondly， the detected single blade instance was divided into foreground and background. By selecting foreground seed points and background seed points， the interactive segmentation method was used to generate complete segmentation results of the blade. After all the blade instances were processed in turn， the final segmentation result of engine blade instance was obtained by merging the results. All the 72 images were used to train the Sparse Instance activation map for real-time instance segmentation （SparseInst）， to produce the initial instance segmentation results. The testing dataset contained 56 images. The mean Average Precision （mAP） of the proposed method is higher than that of SparseInst by 5.1 percentage points. The mAP results of the proposed method are better than those of the state-of-the-art instance segmentation methods， e.g.， MASK R-CNN （Mask Region based Convolutional Neural Network）， YOLACT （You Only Look At CoefficienTs）， BMASK-RCNN （Boundary-preserving MASK R-CNN）.

GIFs （Graphics Interchange Formats） are frequently used as responses to posts on social media platforms， but many approaches do not make good use of the GIF tag information on social media when dealing with the question “how to choose an appropriate GIF to reply to a post”. A Multi-Modal Dialog reply retrieval based on Contrast learning and GIF Tag （CoTa-MMD） approach was proposed， by which the tag information was integrated into the retrieval process. Specifically， the tags were used as intermediate variables， the retrieval of text to GIF was then converted to the retrieval of text to GIF tag to GIF. Then the modal representation was learned by a contrastive learning algorithm and the retrieval probability was calculated using a full probability formula. Compared to direct text image retrieval， the introduction of transition tags reduced retrieval difficulties caused by the heterogeneity of different modalities. Experimental results show that the CoTa-MMD model improved the recall sum of the text image retrieval task by 0.33 percentage points and 4.21 percentage points compared to the DSCMR （Deep Supervised Cross-Modal Retrieval） model on PEPE-56 multimodal dialogue dataset and Taiwan multimodal dialogue dataset， respectively.

Because of the effect of channel and noise, the effect of the traditional pilot jamming scheme will decrease because of the phase deviation. In order to avoid the problem, this paper brings forward a novel scheme based on distribution detection. Firstly, detect the phase deviation of the jamming signal by the terminal distributed in the jamming area; then the result of the detection will be sent to the jamming signal transmitter, it will drive the jamming signal phase equal to the -radian offset of the transmitted pilot tone value. Simulations prove that the scheme is effective on decreasing the phase deviation of the jamming signal.