Attention mechanisms are critical tools for enhancing the performance of convolutional neural networks (CNNs), focusing on spatial and channel dimensions of feature maps, known as spatial attention and channel attention, respectively. While many advanced attention methods combine these dimensions to improve performance, particularly in downstream computer vision tasks, such methods often introduce significant computational overhead or fail to effectively capture long-range spatial dependencies alongside channel attention. To address these challenges, this paper proposes the sequential fusion attention (SFA) method, which introduces a complementary fusion strategy to integrate spatial and channel attention. Spatial attention leverages strip pooling to model long-range dependencies, while channel attention employs dynamic encoding to refine features. By utilizing a grouped processing approach, the SFA module achieves an optimal balance between computational efficiency and representation power. Extensive experiments on benchmark datasets demonstrate that SFA consistently outperforms state-of-the-art attention mechanisms, delivering competitive accuracy in image classification, object detection, and semantic segmentation tasks while maintaining reduced model complexity. This work underscores the potential of lightweight attention mechanisms in modern computer vision and paves the way for further innovations in resource-efficient neural network design. Our code is publicly available at the following URL: https://github.com/Xuwei86/SFA

This paper proposes a method for discovering the primary objects in single images by learning from videos in a purely unsupervised manner—the learning process is based on videos, but the generated network is able to discover objects from a single input image. The rough idea is that an image typically consists of multiple object instances (like the foreground and background) that have spatial transformations across video frames and they can be sparsely represented. By exploring the sparsity representation of a video with a neural network, one may learn the features of each object instance without any labels, which can be used to discover, recognize, or distinguish object instances from a single image. In this paper, we consider a relatively simple scenario, where each image roughly consists of a foreground and a background. Our proposed method is based on encoder-decoder structures to sparsely represent the foreground, background, and segmentation mask, which further reconstruct the original images. We apply the feed-forward network trained from videos for object discovery in single images, which is different from the previous co-segmentation methods that require videos or collections of images as the input for inference. The experimental results on various object segmentation benchmarks demonstrate that the proposed method extracts primary objects accurately and robustly, which suggests that unsupervised image learning tasks can benefit from the sparsity of images and the inter-frame structure of videos.

Generative Artificial Intelligence (GenAI) is revolutionizing the field of higher education by leveraging deep learning models to generate human-like content. However, the use of GenAI in education raises ethical concerns such as the potential impact on critical thinking skills and the unethical or dishonest use by students. This paper analyzed the main problems existing in engineering courses in Chinese universities firstly and then proposed corresponding teaching reform measures based on the comprehensive consideration of the benefits and threats of GenAI. We applied the proposed teaching reform measures in the teaching of engineering courses, which greatly improved the teaching quality.