Accurate tooth identification and delineation in dental CBCT images are essential in clinical oral diagnosis and treatment. Teeth are positioned in the alveolar bone in a particular order, featuring similar appearances across adjacent and bilaterally symmetric teeth. However, existing tooth segmentation methods ignored such specific anatomical topology, which hampers the segmentation accuracy. Here we propose a semantic graph-based method to explicitly model the spatial associations between different anatomical targets (i.e., teeth) for their precise delineation in a coarse-to-fine fashion. First, to efficiently control the bilaterally symmetric confusion in segmentation, we employ a lightweight network to roughly separate teeth as four quadrants. Then, designing a semantic graph attention mechanism to explicitly model the anatomical topology of the teeth in each quadrant, based on which voxel-wise discriminative feature embeddings are learned for the accurate delineation of teeth boundaries. Extensive experiments on a clinical dental CBCT dataset demonstrate the superior performance of the proposed method compared with other state-of-the-art approaches.

Recently, the combination of Transformers and CNNs has witnessed significant advancements in change detection (CD) tasks. However, it remains unexplored how to interactively integrate long-range dependency and local information to enhance the model’s global-local context awareness for effectively mitigating pseudo-changes. In addition, accurate identification and distinction of building changes from complex backgrounds still pose challenges due to the insufficient semantic context modeling across time between bi-temporal images. To address these issues, we propose a hybrid model ConvFormer-CD with parallel convolution and MSA. This combination enables better interaction of global and local information, thereby enhancing the adaptability to complex scenarios. Moreover, we introduce a novel module called Temporal Attention to establish cross-temporal semantic relationships between image pairs, effectively highlighting change regions by learning shared and non-shared semantics. This enables our model to accurately detect changed targets even in scenarios characterized by intricate geo-spatial arrangements and distributions. To further refine the differences in bi-temporal images, we propose a Difference Integration Module (DIM) that connects the encoder and decoder to fuse high-level semantic features across channels. We conduct extensive experiments on four benchmark datasets, including LEVIR-CD, LEVIR-CD+, WHU-CD, and S2Looking-CD, which demonstrates the proposed ConvFormer-CD outperforms other state-of-the-art methods. Our codes will be available at https://github.com/taomi-lab/ConvFormer-CD.

Image retrieval is one of the key techniques of computer vision, and has been studied for a long time. Nevertheless, little attention is paid to infrared and visible cross-modal retrieval which can be widely used in various applications, e.g., infrared and visible surveillance systems. In this paper, we propose a shared features based infrared-visible cross-modal image retrieval method. The similar visual features are extracted from infrared and visible images as the shared features, and the Euclidean distance is used to measure the similarity between these features. The core of the proposed method comes from three aspects: 1) Feature separation network can separate image features into shared features and exclusive features; 2) Maximum Mean Discrepancy (MMD) loss is employed to constrain the distribution of shared features, which can reduce the retrieval error caused by different imaging angles and similarity of infrared images. 3) The cross-layer fusion encoder compensates for the context loss in the convolution of infrared images. Experimental results on the Infrared-Visible dataset demonstrate the proposed method is effective and outperforms the state-of-the-art approaches.

Hyperspectral image (HSI) classification suffers from two serious problems, one is the limited labeled pixels, and the other is the class imbalance problem. As a result, the number of labeled pixels in many categories is not sufficient to characterize the spectral-spatial information, and train a satisfying deep model. By making full use of the information of unlabeled pixels, semi-supervised methods can provide better classification performance in the case of limited labeled pixels. However, they do not take into account the imbalance in the HSI data. As a method of data enhancement, generative adversarial networks focus on the above two problems and have also been widely used for the task of the HSI classification. In this work, we propose a distance constraints-based generative adversarial networks (DGAN) method for HSI classification to address these two problems. The DGAN employs the convolution autoencoder (AE) to extract the latent features of the HSI samples, and considers the reconstructed samples from the AE as the real samples for the later classifier and discriminator. In addition, the DGAN uses two distance constraints to solve the problems of the few labeled samples and class imbalance, the one latent-data distance constraint enforcing the generator to generate HSI samples for each class (especially the minority class), another discriminator-score distance constraint guiding the generator to synthesize samples that resemble the real HSI samples. Finally, the generated samples are combined classwise with the reconstructed samples and the real HSI samples to learn the parameters of the classifier and discriminator. Experimental results show that our method achieves state-of-the-art performance in terms of overall accuracy (OA) when trained with only 0.5%-4% of data sets from Indian Pines, Pavia University, and Botswana. Specifically, our method demonstrates improvements of 5.48%, 8.79%, and 0.91% on these three datasets, respectively. It reveals the great potential of the DGAN model in generating the HSI samples for each class, which contributes to improving the classification performance of the HSI data.

Deep learning has been extensively applied to hyperspectral image (HSI) classification and has achieved significant success. But the number of labeled samples available for HSI classification tasks is typically limited in practical applications, which makes the high-accuracy of HSI small-sample classification still a challenging research task. Therefore, metric-based prototypical networks for few-shot learning have become increasingly popular. However, the majority of existing few-shot learning (FSL) methods typically have the problems with biased prototypes and domain shifts. To address these issues, this paper proposed a prototype rectification network framework for cross-domain few-shot HSI classification. Specifically, to obtain more representative prototypes, we designed a query-guided prototype rectification module, which can rectify the feature distribution of the support set prototype, and obtain a more representative prototype for subsequent training tasks. Then, we introduced a prototype-based inter-class loss function to alleviate the inter-class confusion that may result from prototype rectification. Furthermore, we construct an intermediate domain between the source domain and target domain to alleviate domain shift, which helps mitigate the difficulties of domain transfer and achieve a more comprehensive domain alignment. The experimental results on four publicly available HSI datasets demonstrate that our proposed method outperforms the existing FSL methods.

Due to the difficulty of manually labeling remote sensing scene images and the demand for the ability to recognize new scene classes, few-shot remote sensing scene classification (FSRSSC) has attracted more and more attention. At present, metric-based FSRSSC methods have made promising progress, especially the prototypical networks-based methods. However, due to the complexity of the background of remote sensing scene images, the prototype classifier, which takes the average features of support samples as the metric benchmark, retains the features of category-irrelevant objects and other background information in the image. This leads to a bad classification result. Therefore, in this work, we propose an FSRSSC method based on the deep updated subspace network (DUSN), which uses class subspace as a metric benchmark to represent the commonality of a category and can effectively mitigate the negative impact of irrelevant objects on the classifier. In addition, for the higher interclass similarity and larger intraclass variance of remote sensing scene images, we further propose an interclass constraint and an intraclass constraint to mitigate the classification confusion. We leverage the interclass constraint to make the images of different classes as far apart as possible, and the intraclass constraint to keep the images of the same class clustered as closely together as possible. Experimental results on three public benchmark datasets demonstrate that our method performs better than the state-of-the-art methods for FSRSSC.