Image and sentence matching has attracted increasing attention since it is associated with two important modalities of vision and language. Previous methods aim to find the latent correspondences between image regions and words by aggregating the similarities of the region-word pairs. However, these approaches consider little about the relationships of diverse regions in the image and treat the similarities of all region-word pairs equally. Moreover, focusing on fine-grained alignment overly, the true meaning of the original image will be likely distorted. In this paper, a novel Region Reinforcement Network with Topic Constraint (RRTC) is proposed to explore the correspondences between images and texts. Specifically, the region reinforcement network is built to infer fine-grained correspondence by considering the relationships of regions and re-assigning region-word similarities. Meanwhile, the topic constraint module is presented to summarize the central theme of images, which constrains the original image deviation. Extensive experimental results on MSCOCO and Flickr30k datasets verify the effectiveness of our proposed RRTC.

The field of remote sensing (RS) image change detection (CD) has made significant progress, largely due to the powerful feature representation abilities of deep learning. However, traditional methods have not fully exploited the valuable information in differences. These methods often treat deep models as tools to extract features from individual images, which limits their ability to effectively describe differences. Additionally, many approaches tend to focus on spatial differences, while neglecting variations in the channel dimension. In this study, we introduce a novel Summator–Subtractor network for CD ( ${S}^{2}$ CD), which adeptly captures subtle differences within both the spatial and channel aspects of bi-temporal images. The initial spatial and channel differences are derived through summation and subtraction operations on the bi-temporal images. The summator computes initial channel variations, while the subtractor captures initial spatial disparities. Transformers are then used to pull out meaningful differences in both spatial and channel patterns, allowing for a more nuanced understanding than methods relying solely on features from individual images. Finally, a heterogeneous modulation block integrates channel and spatial difference features, thus amplifying overall differences. Through extensive experimentation on four widely acknowledged CD benchmark datasets, our proposed ${S}^{2}$ CD method outperforms existing techniques, showcasing its superior performance and promising potential. The codes of this work will be available for the sake of reproducibility at: https://github.com/qianday/SSCD-CD .

With the development of deep neural networks, hyperpsectral image (HSI) classification systems have achieved a significant improvement. These systems require numerous and accurate labeled hyperspectral data to be adequately trained. However, noisy labels are inherent in real-world hyperspectral systems, resulting in unreliable decisions. To handle noisy labels in hyperpsectral classification, an end-to-end attentive-adaptive network (AAN) is proposed for robust HSI classification training. The goal is to build a classifier with strong generalization capabilities that can be applied to both clean and noisy training sets without explicit noise label pre-treatment. Specifically, a spectral stem network with non-adjacent shortcut is exploited initially to re-distribute the sensitive layers for noisy labels to achieve robust spectral representation. Then, a group-shuffle attention module is proposed to capture the discriminative and robust spatial-spectral features in the presence of noisy labels. Finally, an adaptive noise-robust loss function is developed to fight against noisy labels by learning a parameter to balance the normalized cross entropy (NCE) and reverse cross entropy (RCE). Experimental results on three HSI benchmark datasets with simulated noisy labels demonstrate the effectiveness of AAN on HSI classification.

In order to make more effective use of Wi-Fi fingerprint data to position an object, an improved adaptive genetic algorithm (IAGA) is proposed to optimize the BP (Back Propagation) neural network, namely, IAGA-BP. In this method, the selection, crossover and mutation operations of the genetic algorithm are used to optimize the weights and biases of the BP neural network. On the one hand, the proposed algorithm improves the selection operator in the adaptive genetic algorithm on the basis of preserving the optimal strategy. That is, the population of each generation will be sorted according to the adaptability from the highest to the lowest, then the highest 20% of the population will be directly inherited to the next generation while the worst 20% will be eliminated. The remaining 80% of the population will be selected by a roulette algorithm based on the selection probability of each individual, as to ensure the population volume unchanging. On the other hand, the crossover and mutation probability formulas in the adaptive genetic algorithm are improved. The crossover and mutation rates will be adjusted to preserve superior individuals and genes according to the level of individual adaptability and the current evolution stage of the population. The simulation results show that compared with the traditional Wi-Fi positioning method, the proposed Wi-Fi positioning method has a faster convergence speed and better positioning accuracy of 2.48 meters.