In the study of incremental object detection, knowledge distillation and data replay are effective methods to mitigate catastrophic forgetting. However, current research on single-stage detectors is limited, single-stage detector outputs often contain excessive negative sample information, and direct application of knowledge distillation to them is ineffective. To address this, this paper proposes a multi-level foreground prompt incremental learning algorithm for single-stage detectors like YOLO, including foreground prompts at the image level, feature map level, and knowledge level. First, to obtain fewer but more representative replay samples, representative images with a high number of old class foregrounds are selected by maximizing sample structure distance, providing direct foreground information at the image level. Second, the feature map output by the teacher model is used as a feature-level prompt, with a feature distillation loss guiding the student model to encode new class foreground information in less significant channels of the old feature map, reducing interference. Lastly, the teacher model’s inference output serves as a knowledge-level prompt, and the adaptive select object method is introduced to avoid foreground conflict in traditional knowledge distillation, enhancing the model’s plasticity by selectively merging foreground information. Extensive experiments on PASCAL VOC and MS COCO datasets demonstrate that this approach significantly improves model plasticity while maintaining stability.

Smart education is an important direction of future educational development, aiming to improve the intelligence level of the existing digital education system and achieve the deep integration of information technology and mainstream education business. Most of the existing cognitive diagnostic models are trained and tested based on the answers to known questions, and the dependence on the trained questions leads to poor prediction results of the model for unknown questions. To solve that problem, this paper divides the questions of the dataset into the usual quizzes (known questions) and the final exam questions (unknown questions), which are used for training and testing, respectively. The cognitive diagnosis model based on a convolutional neural network (CNNCD) is proposed. Firstly, the attention mechanism is used to dig out the intra-layer relationships among students, questions and knowledge points, which alleviates the problem of insufficient information mining among students, questions and knowledge points. Secondly, two multi-layer (general and special) one-dimensional convolutional neural networks are combined to model the cognitive diagnosis of the process from students’ usual grades to students’ mastery of knowledge points. Finally, two multi-layer (general and special) one-dimensional convolutional neural networks are used to predict students’ final exam scores. Experiments on three public datasets and self-made BOIT datasets show that the proposed method is superior to other comparison models on these three evaluation indexes, indicating the feasibility and effectiveness of the proposed model.

For person reidentification (ReID), most slicing methods (such as part‐based convolutional baseline (PCB) and AlignedReID) introduce a lot of background devoid of pedestrian parts, resulting in the cross‐aliasing of features in the deep network. Besides, the resulting component features are not perfectly aligned with each other, thus affecting model performance. We propose a convolutional neural network (CNN) with embedding transformers (CET) person ReID network architecture based on the respective advantages of CNN and transformer. In CET, first, the residual transformer (RT) structure is first embedded in the backbone network of CNN to obtain a feature extractor, named transformers in CNN. The feature aliasing phenomenon is improved by utilizing transformer’s advantage in grasping the relevance of global information. Second, a feature fuse with learnable vector structure for fusing the output vector is added to the output of the transformer at the end of the network. A two branches loss structure is designed to balance the two different fusion strategies. Finally, the self‐attention mechanism in transformer is used for automatic part alignment of human body parts to solve the part alignment problem caused by inaccurate detection frames. The experimental results show that CET network architecture achieves better performance than PCB and some other block‐slicing methods.